Jobs Kyiv, Data Engineer
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Β· 760 views Β· 67 applications Β· 8d
Data Engineer
Countries of Europe or Ukraine Β· 2 years of experience Β· English - B1Looking for a Data Engineer to join the Dataforest team. If you are looking for a friendly team, a healthy working environment, and a flexible schedule β you have found the right place to send your CV. Skills requirements: β’ 2+ years of experience with...Looking for a Data Engineer to join the Dataforest team. If you are looking for a friendly team, a healthy working environment, and a flexible schedule β you have found the right place to send your CV.
Skills requirements:
β’ 2+ years of experience with Python;
β’ 2+ years of experience as a Data Engineer;
β’ Experience with Pandas;
β’ Experience with SQL DB / NoSQL (Redis, Mongo, Elasticsearch) / BigQuery;
β’ Familiarity with Amazon Web Services;
β’ Knowledge of data algorithms and data structures is a MUST;
β’ Working with high volume tables 10m+.
Optional skills (as a plus):
β’ Experience with Spark (pyspark);
β’ Experience with Airflow;
β’ Experience with Kafka;
β’ Experience in statistics;
β’ Knowledge of DS and Machine learning algorithms..Key responsibilities:
β’ Create ETL pipelines and data management solutions (API, Integration logic);
β’ Different data processing algorithms;
β’ Involvement in creation of forecasting, recommendation, and classification models.We offer:
β’ Great networking opportunities with international clients, challenging tasks;
β’ Building interesting projects from scratch using new technologies;
β’ Personal and professional development opportunities;
β’ Competitive salary fixed in USD;
β’ Paid vacation and sick leaves;
β’ Flexible work schedule;
β’ Friendly working environment with minimal hierarchy;
β’ Team building activities, corporate events.
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Β· 23 views Β· 0 applications Β· 4d
Data Quality Engineer
Office Work Β· Ukraine (Kyiv) Β· Product Β· 3 years of experience Β· English - None MilTech πͺWeβre building a large-scale data analytics ecosystem powered by Microsoft Azure and Power BI. Our team integrates, transforms, and visualizes data from multiple sources to support critical business decisions. Data quality is one of our top priorities,...Weβre building a large-scale data analytics ecosystem powered by Microsoft Azure and Power BI. Our team integrates, transforms, and visualizes data from multiple sources to support critical business decisions. Data quality is one of our top priorities, and weβre seeking an engineer who can help us enhance the reliability, transparency, and manageability of our data landscape.
Your responsibilities:
- Develop and maintain data quality monitoring frameworks within the Azure ecosystem (Data Factory, Data Lake, Databricks).
- Design and implement data quality checks, including validation, profiling, cleansing, and standardization.
- Detect data anomalies and design alerting systems (rules, thresholds, automation).
- Collaborate with Data Engineers, Analysts, and Business stakeholders to define data quality criteria and expectations.
- Ensure high data accuracy and integrity for Power BI reports and dashboards.
- Document data validation processes and recommend improvements to data sources.
Requirements:
- 3+ years of experience in a Data Quality, Data Engineering, or BI Engineering role.
- Hands-on experience with Microsoft Azure services (Data Factory, SQL Database, Data Lake).
- Advanced SQL skills (complex queries, optimization, data validation).
- Familiarity with Power BI or similar BI tools.
- Understanding of DWH principles and ETL/ELT pipelines.
- Experience with data quality frameworks and metrics (completeness, consistency, timeliness).
- Knowledge of Data Governance, Master Data Management, and Data Lineage concepts.
Would be a plus:
- Experience with Databricks or Apache Spark.
- DAX and Power Query (M) knowledge.
- Familiarity with DataOps or DevOps principles in a data environment.
- Experience in creating automated data quality dashboards in Power BI.
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Β· 57 views Β· 5 applications Β· 1d
Data Engineer
Hybrid Remote Β· Countries of Europe or Ukraine Β· Product Β· 3 years of experience Β· English - NoneWe are looking for a Data Engineer to build and optimize the data pipelines that fuel our Ukrainian LLM and Kyivstarβs NLP initiatives. In this role, you will design robust ETL/ELT processes to collect, process, and manage large-scale text and metadata,...We are looking for a Data Engineer to build and optimize the data pipelines that fuel our Ukrainian LLM and Kyivstarβs NLP initiatives. In this role, you will design robust ETL/ELT processes to collect, process, and manage large-scale text and metadata, enabling our data scientists and ML engineers to develop cutting-edge language models. You will work at the intersection of data engineering and machine learning, ensuring that our datasets and infrastructure are reliable, scalable, and tailored to the needs of training and evaluating NLP models in a Ukrainian language context. This is a unique opportunity to shape the data foundation of a pioneering AI project in Ukraine, working alongside NLP experts and leveraging modern big data technologies.
What you will do
- Design, develop, and maintain ETL/ELT pipelines for gathering, transforming, and storing large volumes of text data and related information. Ensure pipelines are efficient and can handle data from diverse sources (e.g., web crawls, public datasets, internal databases) while maintaining data integrity.
- Implement web scraping and data collection services to automate the ingestion of text and linguistic data from the web and other external sources. This includes writing crawlers or using APIs to continuously collect data relevant to our language modeling efforts.
- Implementation of NLP/LLM-specific data processing: cleaning and normalization of text, like filtering of toxic content, de-duplication, de-noising, detection, and deletion of personal data.
- Formation of specific SFT/RLHF datasets from existing data, including data augmentation/labeling with LLM as teacher.
- Set up and manage cloud-based data infrastructure for the project. Configure and maintain data storage solutions (data lakes, warehouses) and processing frameworks (e.g., distributed compute on AWS/GCP/Azure) that can scale with growing data needs.
- Automate data processing workflows and ensure their scalability and reliability. Use workflow orchestration tools like Apache Airflow to schedule and monitor data pipelines, enabling continuous and repeatable model training and evaluation cycles.
- Maintain and optimize analytical databases and data access layers for both ad-hoc analysis and model training needs. Work with relational databases (e.g., PostgreSQL) and other storage systems to ensure fast query performance and well-structured data schemas.
- Collaborate with Data Scientists and NLP Engineers to build data features and datasets for machine learning models. Provide data subsets, aggregations, or preprocessing as needed for tasks such as language model training, embedding generation, and evaluation.
- Implement data quality checks, monitoring, and alerting. Develop scripts or use tools to validate data completeness and correctness (e.g., ensuring no critical data gaps or anomalies in the text corpora), and promptly address any pipeline failures or data issues. Implement data version control.
- Manage data security, access, and compliance. Control permissions to datasets and ensure adherence to data privacy policies and security standards, especially when dealing with user data or proprietary text sources.
Qualifications and experience needed
- Education & Experience: 3+ years of experience as a Data Engineer or in a similar role, building data-intensive pipelines or platforms. A Bachelorβs or Masterβs degree in Computer Science, Engineering, or a related field is preferred. Experience supporting machine learning or analytics teams with data pipelines is a strong advantage.
- NLP Domain Experience: Prior experience handling linguistic data or supporting NLP projects (e.g., text normalization, handling different encodings, tokenization strategies). Knowledge of Ukrainian text sources and data sets, or experience with multilingual data processing, can be an advantage given our projectβs focus. Understanding of FineWeb2 or a similar processing pipeline approach.
- Data Pipeline Expertise: Hands-on experience designing ETL/ELT processes, including extracting data from various sources, using transformation tools, and loading into storage systems. Proficiency with orchestration frameworks like Apache Airflow for scheduling workflows. Familiarity with building pipelines for unstructured data (text, logs) as well as structured data.
- Programming & Scripting: Strong programming skills in Python for data manipulation and pipeline development. Experience with NLP packages (spaCy, NLTK, langdetect, fasttext, etc.). Experience with SQL for querying and transforming data in relational databases. Knowledge of Bash or other scripting for automation tasks. Writing clean, maintainable code and using version control (Git) for collaborative development.
- Databases & Storage: Experience working with relational databases (e.g., PostgreSQL, MySQL), including schema design and query optimization. Familiarity with NoSQL or document stores (e.g., MongoDB) and big data technologies (HDFS, Hive, Spark) for large-scale data is a plus. Understanding of or experience with vector databases (e.g., Pinecone, FAISS) is beneficial, as our NLP applications may require embedding storage and fast similarity search.
- Cloud Infrastructure: Practical experience with cloud platforms (AWS, GCP, or Azure) for data storage and processing. Ability to set up services such as S3/Cloud Storage, data warehouses (e.g., BigQuery, Redshift), and use cloud-based ETL tools or serverless functions. Understanding of infrastructure-as-code (Terraform, CloudFormation) to manage resources is a plus.
- Data Quality & Monitoring: Knowledge of data quality assurance practices. Experience implementing monitoring for data pipelines (logs, alerts) and using CI/CD tools to automate pipeline deployment and testing. An analytical mindset to troubleshoot data discrepancies and optimize performance bottlenecks.
- Collaboration & Domain Knowledge: Ability to work closely with data scientists and understand the requirements of machine learning projects. Basic understanding of NLP concepts and the data needs for training language models, so you can anticipate and accommodate the specific forms of text data and preprocessing they require. Good communication skills to document data workflows and to coordinate with team members across different functions.
A plus would be
- Advanced Tools & Frameworks: Experience with distributed data processing frameworks (such as Apache Spark or Databricks) for large-scale data transformation, and with message streaming systems (Kafka, Pub/Sub) for real-time data pipelines. Familiarity with data serialization formats (JSON, Parquet) and handling of large text corpora.
- Web Scraping Expertise: Deep experience in web scraping, using tools like Scrapy, Selenium, or Beautiful Soup, and handling anti-scraping challenges (rotating proxies, rate limiting). Ability to parse and clean raw text data from HTML, PDFs, or scanned documents.
- CI/CD & DevOps: Knowledge of setting up CI/CD pipelines for data engineering (using GitHub Actions, Jenkins, or GitLab CI) to test and deploy changes to data workflows. Experience with containerization (Docker) to package data jobs and with Kubernetes for scaling them is a plus.
- Big Data & Analytics: Experience with analytics platforms and BI tools (e.g., Tableau, Looker) used to examine the data prepared by the pipelines. Understanding of how to create and manage data warehouses or data marts for analytical consumption.
- Problem-Solving: Demonstrated ability to work independently in solving complex data engineering problems, optimising existing pipelines, and implementing new ones under time constraints. A proactive attitude to explore new data tools or techniques that could improve our workflows.
What we offer
- Office or remote β itβs up to you. You can work from anywhere, and we will arrange your workplace.
- Remote onboarding.
- Performance bonuses.
- We train employees with the opportunity to learn through the companyβs library, internal resources, and programs from partners.β―
- Health and life insurance.
- Wellbeing program and corporate psychologist.
- Reimbursement of expenses for Kyivstar mobile communication.
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Β· 22 views Β· 0 applications Β· 7d
Middle/Senior/Lead Python Cloud Engineer (IRC280058)
Hybrid Remote Β· Ukraine Β· 5 years of experience Β· English - B2Job Description β’ Terraform β’ AWS Platform: Working experience with AWS services - in particular serverless architectures (S3, RDS, Lambda, IAM, API Gateway, etc.) supporting API development in a microservices architecture β’ Programming Languages: Python...Job Description
β’ Terraform
β’ AWS Platform: Working experience with AWS services - in particular serverless architectures (S3, RDS, Lambda, IAM, API Gateway, etc.) supporting API development in a microservices architecture
β’ Programming Languages: Python (strong programming skills)
β’ Data Formats: Experience with JSON, XML, and other relevant data formats
β’ CI/CD Tools: experience setting up and managing CI/CD pipelines using GitLab CI, Jenkins, or similar tools
β’ Scripting and automation: experience in scripting languages such as Python, PowerShell, etc.
β’ Monitoring and Logging: Familiarity with monitoring & logging tools like CloudWatch, ELK, Dynatrace, Prometheus, etcβ¦
β’ Source Code Management: Expertise with git commands and associated VCS (Gitlab, Github, Gitea, or similar)
NICE TO HAVE
β’ Strongly Preferred: Infrastructure as Code: Experience with Terraform and CloudFormation - Proven ability to write and manage Infrastructure as Code (IaC)
β’ Documentation: Experience with markdown and, in particular, Antora for creating technical documentation
β’ Experience working with Healthcare Data, including HL7v2, FHI,R and DICOM
β’ FHIR and/or HL7 Certifications
β’ Building software classified as Software as a Medical Device (SaMD)
β’ Understanding of EHR technologies such as EPIC, Cerner, e.c.
β’ Experience in implementing enterprise-grade cyber security & privacy by design into software products
β’ Experience working in Digital Health software
β’ Experience developing global applications
β’ Strong understanding of SDLC β Waterfall & Agile methodologies
β’ Software estimation
β’ Experience leading software development teams onshore and offshoreJob Responsibilities
β’ Develops, documents, and configures systems specifications that conform to defined architecture standards, address business requirements, and processes in the cloud development & engineering.
β’ Involved in planning of system and development deployment, as well as responsible for meeting compliance and security standards.
β’ API development using AWS services
β’ Experience with Infrastructure as Code (IaC)
β’ Actively identifies system functionality or performance deficiencies, executes changes to existing systems, and tests functionality of the system to correct deficiencies and maintain more effective data handling, data integrity, conversion, input/output requirements, and storage.
β’ May document testing and maintenance of system updates, modifications, and configurations.
β’ May act as a liaison with key technology vendor technologists or other business functions.
β’ Function Specific: Strategically design technology solutions that meet the needs and goals of the company and its customers/users.
β’ Leverages platform process expertise to assess if existing standard platform functionality will solve a business problem or customization solution would be required.
β’ Test the quality of a product and its ability to perform a task or solve a problem.
β’ Perform basic maintenance and performance optimization procedures in each of the primary operating systems.
β’ Ability to document detailed technical system specifications based on business system requirements
β’ Ensures system implementation compliance with global & local regulatory and security standards (i.e. , HIPAA, SOCII, ISO27001, etc.)Department/Project Description
The Digital Health organization is a technology team that focuses on next-generation Digital Health capabilities, which deliver on the Medicine mission and vision to deliver Insight Driven Care. This role will operate within the Digital Health Applications & Interoperability subgroup of the broader Digital Health team, focused on patient engagement, care coordination, AI, healthcare analytics & interoperability amongst other advanced technologies which enhance our product portfolio with new services, while improving clinical & patient experiences.
Authorization and Authentication platform & services for Digital Health
Secure cloud platform for storing and managing medical images (DICOM compliant). Leverages AWS for cost-effective storage and access, integrates with existing systems (EHR, PACS), and offers a customizable user interface.
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Β· 42 views Β· 3 applications Β· 17d
Senior Data Engineer
Hybrid Remote Β· Ukraine Β· Product Β· 4 years of experience Β· English - B2Your future responsibilities: Collaborate with data and analytics experts to strive for greater functionality in our data systems Design, use and test the infrastructure required for optimal extraction, transformation, and loading of data from a wide...Your future responsibilities:
- Collaborate with data and analytics experts to strive for greater functionality in our data systems
- Design, use and test the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS big data technologies (DevOps & Continuous Integration)
- Drive the advancement of data infrastructure by designing and implementing the underlying logic and structure for how data is set up, cleansed, and ultimately stored for organizational usage
- Assemble large, complex data sets that meet functional / non-functional business requirements
- Build data integration from various sources and technologies to the data lake infrastructure as part of an agile delivery team
- Monitor the capabilities and react on unplanned interruptions ensuring that environments are provided & loaded in time
Your skills and experience:
- Minimum 5 years experience in a dedicated data engineer role
- Experience working with large structured and unstructured data in various formats
- Knowledge or experience with streaming data frameworks and distributed data architectures (e.g. Spark Structured Streaming, Apache Beam or Apache Flink)
- Experience with cloud technologies (preferable AWS, Azure)
- Experience in Cloud services (Data Flow, Data Proc, BigQuery, Pub/Sub)
- Experience of practical operation of Big Data stack: Hadoop, HDFS, Hive, Presto, Kafka
- Experience of Python in the context of creating ETL data pipelines
- Experience with Data Lake / Data Warehouse solutions (AWS S3 // Minio)
- Experience with Apache Airflow
- Development skills in a Docker / Kubernetes environment
- Open and team-minded personality and communication skills
- Willingness to work in an agile environment
We offer what matters most to you:
- Competitive salary: we guarantee a stable income and annual bonuses for your personal contribution. Additionally, we have a referral program with rewards for bringing in new colleagues to Raiffeisen Bank
- Social package: official employment, 28 days of paid leave, additional paternity leave, and financial assistance for parents with newborns
- Comfortable working conditions: possibility of a hybrid work format, offices equipped with shelters and generators, modern equipment. Classification: PUBLIC
- Wellbeing program: all employees have access to medical insurance from the first working day; consultations with a psychologist, nutritionist, or lawyer; discount programs for sports and purchases; family days for children and adults; in-office massages
- Training and development: access to over 130 online training resources; corporate training programs in CX, Data, IT Security, Leadership, Agile. Corporate library and English lessons. β’ Great team: our colleagues form a community where curiosity, talent, and innovation are welcome. We support each other, learn together, and grow. You can find like-minded individuals in over 15 professional communities, reading clubs, or sports clubs
- Career opportunities: we encourage advancement within the bank across functions
- Innovations and technologies: Infrastructure: AWS, Kubernetes, Docker, GitHub, GitHub actions, ArgoCD, Prometheus, Victoria, Vault, OpenTelemetry, ElasticSearch, Crossplain, Grafana. Languages: Java (main), Python (data), Go (infra, security), Swift (IOS), Kotlin (Android). Data stores: Sql-Oracle, PgSql, MsSql, Sybase. Data management: Kafka, AirFlow, Spark, Flink
- Support program for defenders: we maintain jobs and pay average wages to mobilized individuals. For veterans, we have a support program and develop the Bankβs veterans community. We work on increasing awareness among leaders and teams about the return of veterans to civilian life. Raiffeisen Bank has been recognized as one of the best employers for veterans by Forbes
Why Raiffeisen Bank?
- Our main value is people, and we support and recognize them, educate them and involve them in changes. Join Raifβs team because for us YOU matter!
- One of the largest lenders to the economy and agricultural business among private banks
- Recognized as the best employer by EY, Forbes, Randstad, Franklin Covey, and Delo.UA
- The largest humanitarian aid donor among banks (Ukrainian Red Cross, UNITED24, Superhumans, Π‘ΠΠΠΠΠΠ)
- One of the largest IT product teams among the countryβs banks. β’ One of the largest taxpayers in Ukraine; 6.6 billion UAH were paid in taxes in 2023
Opportunities for Everyone:
- Rife is guided by principles that focus on people and their development, with 5,500 employees and more than 2.7 million customers at the center of attention
- We support the principles of diversity, equality and inclusiveness
- We are open to hiring veterans and people with disabilities and are ready to adapt the work environment to your special needs
- We cooperate with students and older people, creating conditions for growth at any career stage
Want to learn more? β Follow us on social media:
Facebook, Instagram, LinkedIn
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Π Π°ΠΉΡΡΠ°ΠΉΠ·Π΅Π½ ΠΠ°Π½ΠΊ β Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΈΠΉ ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ Π±Π°Π½ΠΊ Π· ΡΠ½ΠΎΠ·Π΅ΠΌΠ½ΠΈΠΌ ΠΊΠ°ΠΏΡΡΠ°Π»ΠΎΠΌ. ΠΡΠ»ΡΡΠ΅ 30 ΡΠΎΠΊΡΠ² ΠΌΠΈ ΡΡΠ²ΠΎΡΡΡΠΌΠΎ ΡΠ° Π²ΠΈΠ±ΡΠ΄ΠΎΠ²ΡΡΠΌΠΎ Π±Π°Π½ΠΊΡΠ²ΡΡΠΊΡ ΡΠΈΡΡΠ΅ΠΌΡ Π½Π°ΡΠΎΡ Π΄Π΅ΡΠΆΠ°Π²ΠΈ.
Π£ Π Π°ΠΉΡΡ ΠΏΡΠ°ΡΡΡ ΠΏΠΎΠ½Π°Π΄ 5 500 ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ², ΡΠ΅ΡΠ΅Π΄ Π½ΠΈΡ ΠΎΠ΄Π½Π° ΡΠ· Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΈΡ ΠΠ’-ΠΊΠΎΠΌΠ°Π½Π΄, ΡΠΎ Π½Π°Π»ΡΡΡΡ ΠΏΠΎΠ½Π°Π΄ 800 ΡΠ°Ρ ΡΠ²ΡΡΠ². Π©ΠΎΠ΄Π½Ρ ΠΏΠ»ΡΡ-ΠΎ-ΠΏΠ»ΡΡ ΠΌΠΈ ΠΏΡΠ°ΡΡΡΠΌΠΎ, ΡΠΎΠ± Π±ΡΠ»ΡΡ Π½ΡΠΆ 2,7 ΠΌΡΠ»ΡΠΉΠΎΠ½Π° Π½Π°ΡΠΈΡ ΠΊΠ»ΡΡΠ½ΡΡΠ² ΠΌΠΎΠ³Π»ΠΈ ΠΎΡΡΠΈΠΌΠ°ΡΠΈ ΡΠΊΡΡΠ½Π΅ ΠΎΠ±ΡΠ»ΡΠ³ΠΎΠ²ΡΠ²Π°Π½Π½Ρ, ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΠΈΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠ°ΠΌΠΈ Ρ ΡΠ΅ΡΠ²ΡΡΠ°ΠΌΠΈ Π±Π°Π½ΠΊΡ, ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ Π±ΡΠ·Π½Π΅Ρ, Π°Π΄ΠΆΠ΅ ΠΌΠΈ #Π Π°Π·ΠΎΠΌ_Π·_Π£ΠΊΡΠ°ΡΠ½ΠΎΡ.β―
Π’Π²ΠΎΡ ΠΌΠ°ΠΉΠ±ΡΡΠ½Ρ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ:
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· Π΅ΠΊΡΠΏΠ΅ΡΡΠ°ΠΌΠΈ Π· Π΄Π°Π½ΠΈΡ ΡΠ° Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ, ΡΠΎΠ± Π΄ΠΎΡΡΠ³ΡΠΈ Π±ΡΠ»ΡΡΠΎΡ ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½ΠΎΡΡΡ Π½Π°ΡΠΈΡ ΡΠΈΡΡΠ΅ΠΌ Π΄Π°Π½ΠΈΡ
- ΠΡΠΎΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ, Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠ° ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠΈ, Π½Π΅ΠΎΠ±Ρ ΡΠ΄Π½ΠΎΡ Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΠΈΠ»ΡΡΠ΅Π½Π½Ρ, ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΠ° Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ Π΄Π°Π½ΠΈΡ Π· ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΠΊΡΡΡ Π΄ΠΆΠ΅ΡΠ΅Π» Π΄Π°Π½ΠΈΡ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ SQL ΡΠ° AWS Π΄Π»Ρ Π²Π΅Π»ΠΈΠΊΠΈΡ Π΄Π°Π½ΠΈΡ (DevOps ΡΠ° Π±Π΅Π·ΠΏΠ΅ΡΠ΅ΡΠ²Π½Π° ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ)
- Π‘ΠΏΡΠΈΡΠ½Π½Ρ ΡΠΎΠ·Π²ΠΈΡΠΊΡ ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠΈ Π΄Π°Π½ΠΈΡ ΡΠ»ΡΡ ΠΎΠΌ ΠΏΡΠΎΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ Π±Π°Π·ΠΎΠ²ΠΎΡ Π»ΠΎΠ³ΡΠΊΠΈ ΡΠ° ΡΡΡΡΠΊΡΡΡΠΈ Π΄Π»Ρ Π½Π°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ, ΠΎΡΠΈΡΠ΅Π½Π½Ρ ΡΠ°, Π·ΡΠ΅ΡΡΠΎΡ, Π·Π±Π΅ΡΡΠ³Π°Π½Π½Ρ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Π² ΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΡΡ
- ΠΠ±ΠΈΡΠ°ΡΠΈ Π²Π΅Π»ΠΈΠΊΡ, ΡΠΊΠ»Π°Π΄Π½Ρ Π½Π°Π±ΠΎΡΠΈ Π΄Π°Π½ΠΈΡ , ΡΠΎ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΡ ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΠΌ/Π½Π΅ΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΠΌ Π±ΡΠ·Π½Π΅Ρ-Π²ΠΈΠΌΠΎΠ³Π°ΠΌ
- Π‘ΡΠ²ΠΎΡΡΠ²Π°ΡΠΈ ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ Π΄Π°Π½ΠΈΡ Π· ΡΡΠ·Π½ΠΈΡ Π΄ΠΆΠ΅ΡΠ΅Π» ΡΠ° ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ Π² ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ ΠΎΠ·Π΅ΡΠ° Π΄Π°Π½ΠΈΡ ΡΠΊ ΡΠ°ΡΡΠΈΠ½Π° Π³Π½ΡΡΠΊΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Π· ΠΏΠΎΡΡΠ°ΡΠ°Π½Π½Ρ
- ΠΠΎΠ½ΡΡΠΎΡΠΈΡΠΈ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ ΡΠ° ΡΠ΅Π°Π³ΡΠ²Π°ΡΠΈ Π½Π° Π½Π΅Π·Π°ΠΏΠ»Π°Π½ΠΎΠ²Π°Π½Ρ ΠΏΠ΅ΡΠ΅Π±ΠΎΡ, Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΡΡΡΠΈ ΡΠ²ΠΎΡΡΠ°ΡΠ½Π΅ Π½Π°Π΄Π°Π½Π½Ρ ΡΠ° Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡ
Π’Π²ΡΠΉ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠ° Π½Π°Π²ΠΈΡΠΊΠΈ:
- ΠΡΠ½ΡΠΌΡΠΌ 5 ΡΠΎΠΊΡΠ² Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ±ΠΎΡΠΈ Π½Π° ΠΏΠΎΡΠ°Π΄Ρ ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ ΡΠ½ΠΆΠ΅Π½Π΅ΡΠ° Π· Π΄Π°Π½ΠΈΡ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΡΡΡΡΠΊΡΡΡΠΎΠ²Π°Π½ΠΈΠΌΠΈ ΡΠ° Π½Π΅ΡΡΡΡΠΊΡΡΡΠΎΠ²Π°Π½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ Π² ΡΡΠ·Π½ΠΈΡ ΡΠΎΡΠΌΠ°ΡΠ°Ρ
- ΠΠ½Π°Π½Π½Ρ Π°Π±ΠΎ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊΠ°ΠΌΠΈ ΠΏΠΎΡΠΎΠΊΠΎΠ²ΠΈΡ Π΄Π°Π½ΠΈΡ ΡΠ° ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ»Π΅Π½ΠΈΠΌΠΈ Π°ΡΡ ΡΡΠ΅ΠΊΡΡΡΠ°ΠΌΠΈ Π΄Π°Π½ΠΈΡ (Π½Π°ΠΏΡΠΈΠΊΠ»Π°Π΄,
- Spark Structured Streaming, Apache Beam Π°Π±ΠΎ Apache Flink)
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Ρ ΠΌΠ°ΡΠ½ΠΈΠΌΠΈ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠΌΠΈ (Π±Π°ΠΆΠ°Π½ΠΎ AWS, Azure)
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Ρ ΠΌΠ°ΡΠ½ΠΈΠΌΠΈ ΡΠ΅ΡΠ²ΡΡΠ°ΠΌΠΈ (Data Flow, Data Proc, BigQuery, Pub/Sub)
- ΠΠΎΡΠ²ΡΠ΄ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΎΡ Π΅ΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΡΡ ΡΡΠ΅ΠΊΡ Big Data: Hadoop, HDFS, Hive, Presto, Kafka
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Python Ρ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ETL-ΠΏΠΎΡΠΎΠΊΡΠ² Π΄Π°Π½ΠΈΡ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΡΡΠ΅Π½Π½ΡΠΌΠΈ Data Lake / Data Warehouse (AWS S3 // Minio)
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Apache Airflow
- ΠΠ°Π²ΠΈΡΠΊΠΈ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ Π² ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΡ Docker / Kubernetes
- ΠΡΠ΄ΠΊΡΠΈΡΠ° ΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄Π½Π° ΠΎΡΠΎΠ±ΠΈΡΡΡΡΡΡ, ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΠΈΠ²Π½Ρ Π½Π°Π²ΠΈΡΠΊΠΈ
- ΠΠΎΡΠΎΠ²Π½ΡΡΡΡ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π² Π³Π½ΡΡΠΊΠΎΠΌΡ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΡ
ΠΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ ΡΠ΅, ΡΠΎ ΠΌΠ°Ρ Π·Π½Π°ΡΠ΅Π½Π½Ρ ΡΠ°ΠΌΠ΅ Π΄Π»Ρ ΡΠ΅Π±Π΅:β―
- ΠΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Π° Π·Π°ΡΠΎΠ±ΡΡΠ½Π° ΠΏΠ»Π°ΡΠ°: Π³Π°ΡΠ°Π½ΡΡΡΠΌΠΎ ΡΡΠ°Π±ΡΠ»ΡΠ½ΠΈΠΉ Π΄ΠΎΡ ΡΠ΄ ΡΠ° ΡΡΡΠ½Ρ Π±ΠΎΠ½ΡΡΠΈ Π·Π° ΡΠ²ΡΠΉ ΠΎΡΠΎΠ±ΠΈΡΡΠΈΠΉ Π²Π½Π΅ΡΠΎΠΊ. ΠΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΎ, Ρ Π½Π°Ρ Π΄ΡΡ ΡΠ΅ΡΠ΅ΡΠ°Π»ΡΠ½Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ° Π²ΠΈΠ½Π°Π³ΠΎΡΠΎΠ΄ΠΈ Π·Π° Π·Π°Π»ΡΡΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΠΊΠΎΠ»Π΅Π³ Π΄ΠΎ Π Π°ΠΉΡΡΠ°ΠΉΠ·Π΅Π½ ΠΠ°Π½ΠΊΡ.
- Π‘ΠΎΡΡΠ°Π»ΡΠ½ΠΈΠΉ ΠΏΠ°ΠΊΠ΅Ρ: ΠΎΡΡΡΡΠΉΠ½Π΅ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ, 28 Π΄Π½ΡΠ² ΠΎΠΏΠ»Π°ΡΡΠ²Π°Π½ΠΎΡ Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ, Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΈΠΉ βΠ΄Π΅ΠΊΡΠ΅Ρβ Π΄Π»Ρ ΡΠ°ΡΡΡΡΠ², ΡΠ° ΠΌΠ°ΡΠ΅ΡΡΠ°Π»ΡΠ½Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³Π° Π΄Π»Ρ Π±Π°ΡΡΠΊΡΠ² ΠΏΡΠΈ Π½Π°ΡΠΎΠ΄ΠΆΠ΅Π½Π½Ρ Π΄ΡΡΠ΅ΠΉ.
- ΠΠΎΠΌΡΠΎΡΡΠ½Ρ ΡΠΌΠΎΠ²ΠΈ ΠΏΡΠ°ΡΡ: ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π³ΡΠ±ΡΠΈΠ΄Π½ΠΎΠ³ΠΎ ΡΠΎΡΠΌΠ°ΡΡ ΡΠΎΠ±ΠΎΡΠΈ, ΠΎΡΡΡΠΈ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΠΊΡΠΈΡΡΡΠΌΠΈ ΡΠ° Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠ°ΠΌΠΈ, Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΡΡΠ°ΡΠ½ΠΎΡ ΡΠ΅Ρ Π½ΡΠΊΠΎΡ.
- Wellbeing ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ°: Π΄Π»Ρ Π²ΡΡΡ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ² Π΄ΠΎΡΡΡΠΏΠ½Ρ ΠΌΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ Π· ΠΏΠ΅ΡΡΠΎΠ³ΠΎ ΡΠΎΠ±ΠΎΡΠΎΠ³ΠΎ Π΄Π½Ρ; ΠΊΠΎΠ½ΡΡΠ»ΡΡΠ°ΡΡΡ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³Π°, Π½ΡΡΡΠΈΡΡΠΎΠ»ΠΎΠ³Π° ΡΠΈ ΡΡΠΈΡΡΠ°; Π΄ΠΈΡΠΊΠΎΠ½Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΈ Π½Π° ΡΠΏΠΎΡΡ ΡΠ° ΠΏΠΎΠΊΡΠΏΠΊΠΈ; family days Π΄Π»Ρ Π΄ΡΡΠ΅ΠΉ ΡΠ° Π΄ΠΎΡΠΎΡΠ»ΠΈΡ ; ΠΌΠ°ΡΠ°ΠΆ Π² ΠΎΡΡΡΡ.
- ΠΠ°Π²ΡΠ°Π½Π½Ρ ΡΠ° ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ: Π΄ΠΎΡΡΡΠΏ Π΄ΠΎ ΠΏΠΎΠ½Π°Π΄ 130 Π½Π°Π²ΡΠ°Π»ΡΠ½ΠΈΡ ΠΎΠ½Π»Π°ΠΉΠ½-ΡΠ΅ΡΡΡΡΡΠ²; ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ Π½Π°Π²ΡΠ°Π»ΡΠ½Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΈ Π· CX, Data, IT Security, ΠΡΠ΄Π΅ΡΡΡΠ²Π°, Agile. ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Π° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠ° ΡΠ° ΡΡΠΎΠΊΠΈ Π°Π½Π³Π»ΡΠΉΡΡΠΊΠΎΡ.
- ΠΡΡΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄Π°: Π½Π°ΡΡ ΠΊΠΎΠ»Π΅Π³ΠΈ β ΡΠ΅ ΡΠΏΡΠ»ΡΠ½ΠΎΡΠ°, Π΄Π΅ Π²ΡΡΠ°ΡΡΡΡΡ Π΄ΠΎΠΏΠΈΡΠ»ΠΈΠ²ΡΡΡΡ, ΡΠ°Π»Π°Π½Ρ ΡΠ° ΡΠ½Π½ΠΎΠ²Π°ΡΡΡ. ΠΠΈ ΠΏΡΠ΄ΡΡΠΈΠΌΡΡΠΌΠΎ ΠΎΠ΄ΠΈΠ½ ΠΎΠ΄Π½ΠΎΠ³ΠΎ, Π²ΡΠΈΠΌΠΎΡΡ ΡΠ°Π·ΠΎΠΌ ΡΠ° Π·ΡΠΎΡΡΠ°ΡΠΌΠΎ. Π’ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π·Π½Π°ΠΉΡΠΈ ΠΎΠ΄Π½ΠΎΠ΄ΡΠΌΡΡΠ² Ρ ΠΏΠΎΠ½Π°Π΄ 15-ΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΡ ΠΊΠΎΠΌβΡΠ½ΡΡΡ, ΡΠΈΡΠ°ΡΡΠΊΠΎΠΌΡ ΡΠΈ ΡΠΏΠΎΡΡΠΈΠ²Π½ΠΎΠΌΡ ΠΊΠ»ΡΠ±Π°Ρ .
- ΠΠ°ΡβΡΡΠ½Ρ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ: ΠΌΠΈ Π·Π°ΠΎΡ ΠΎΡΡΡΠΌΠΎ ΠΏΡΠΎΡΡΠ²Π°Π½Π½Ρ Π²ΡΠ΅ΡΠ΅Π΄ΠΈΠ½Ρ Π±Π°Π½ΠΊΡ ΠΌΡΠΆ ΡΡΠ½ΠΊΡΡΡΠΌΠΈ.
- ΠΠ½Π½ΠΎΠ²Π°ΡΡΡ ΡΠ° ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ. Infrastructure: AWS, Kubernetes, Docker, GitHub, GitHub actions, ArgoCD, Prometheus, Victoria, Vault, OpenTelemetry, ElasticSearch, Crossplain, Grafana. Languages: Java (main), Python (data), Go(infra,security), Swift (IOS), Kotlin (Andorid). Datastores: Sql-Oracle, PgSql, MsSql, Sybase. Data management: Kafka, AirFlow, Spark, Flink.
- ΠΡΠΎΠ³ΡΠ°ΠΌΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ Π·Π°Ρ ΠΈΡΠ½ΠΈΠΊΡΠ² Ρ Π·Π°Ρ ΠΈΡΠ½ΠΈΡΡ: ΠΌΠΈ Π·Π±Π΅ΡΡΠ³Π°ΡΠΌΠΎ ΡΠΎΠ±ΠΎΡΡ ΠΌΡΡΡΡ ΡΠ° Π²ΠΈΠΏΠ»Π°ΡΡΡΠΌΠΎ ΡΠ΅ΡΠ΅Π΄Π½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½Ρ ΠΏΠ»Π°ΡΡ ΠΌΠΎΠ±ΡΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΌ. ΠΠ»Ρ Π²Π΅ΡΠ΅ΡΠ°Π½ΡΠ² ΡΠ° Π²Π΅ΡΠ΅ΡΠ°Π½ΠΎΠΊ Ρ Π½Π°Ρ Π΄ΡΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ, ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΡΡΡΡ Π²Π΅ΡΠ΅ΡΠ°Π½ΡΡΠΊΠ° ΡΠΏΡΠ»ΡΠ½ΠΎΡΠ° ΠΠ°Π½ΠΊΡ. ΠΠΈ ΠΏΡΠ°ΡΡΡΠΌΠΎ Π½Π°Π΄ ΠΏΡΠ΄Π²ΠΈΡΠ΅Π½Π½ΡΠΌ ΠΎΠ±ΡΠ·Π½Π°Π½ΠΎΡΡΡ ΠΊΠ΅ΡΡΠ²Π½ΠΈΠΊΡΠ² ΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄ Π· ΠΏΠΈΡΠ°Π½Ρ ΠΏΠΎΠ²Π΅ΡΠ½Π΅Π½Π½Ρ Π²Π΅ΡΠ΅ΡΠ°Π½ΡΠ² Π΄ΠΎ ΡΠΈΠ²ΡΠ»ΡΠ½ΠΎΠ³ΠΎ ΠΆΠΈΡΡΡ. Π Π°ΠΉΡΡΠ°ΠΉΠ·Π΅Π½ ΠΠ°Π½ΠΊ Π²ΡΠ΄Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΡΠΊ ΠΎΠ΄ΠΈΠ½ Π· Π½Π°ΠΉΠΊΡΠ°ΡΠΈΡ ΡΠΎΠ±ΠΎΡΠΎΠ΄Π°Π²ΡΡΠ² Π΄Π»Ρ Π²Π΅ΡΠ΅ΡΠ°Π½ΡΠ² (Forbes).
Π§ΠΎΠΌΡ Π Π°ΠΉΡΡΠ°ΠΉΠ·Π΅Π½ ΠΠ°Π½ΠΊ?β―
- ΠΠ°ΡΠ° Π³ΠΎΠ»ΠΎΠ²Π½Π° ΡΡΠ½Π½ΡΡΡΡ β Π»ΡΠ΄ΠΈ Ρ ΠΌΠΈ Π΄Π°ΡΠΌΠΎ ΡΠΌ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΡ Ρ Π²ΠΈΠ·Π½Π°Π½Π½Ρ, Π½Π°Π²ΡΠ°ΡΠΌΠΎ, Π·Π°Π»ΡΡΠ°ΡΠΌΠΎ Π΄ΠΎ Π·ΠΌΡΠ½. ΠΡΠΈΡΠ΄Π½ΡΠΉΡΡ Π΄ΠΎ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Π Π°ΠΉΡΡ, Π°Π΄ΠΆΠ΅ Π΄Π»Ρ Π½Π°Ρ Π’Π ΠΌΠ°ΡΡ Π·Π½Π°ΡΠ΅Π½Π½Ρ!β―
- ΠΠ΄ΠΈΠ½ ΡΠ· Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΈΡ ΠΊΡΠ΅Π΄ΠΈΡΠΎΡΡΠ² Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈ ΡΠ° Π°Π³ΡΠ°ΡΠ½ΠΎΠ³ΠΎ Π±ΡΠ·Π½Π΅ΡΡ ΡΠ΅ΡΠ΅Π΄ ΠΏΡΠΈΠ²Π°ΡΠ½ΠΈΡ Π±Π°Π½ΠΊΡΠ²β―
- ΠΠΈΠ·Π½Π°Π½ΠΈΠΉ Π½Π°ΠΉΠΊΡΠ°ΡΠΈΠΌ ΠΏΡΠ°ΡΠ΅Π΄Π°Π²ΡΠ΅ΠΌ Π·Π° Π²Π΅ΡΡΡΡΠΌΠΈ EY, Forbes, Randstad, Franklin Covey, Delo.UAβ―
- ΠΠ°ΠΉΠ±ΡΠ»ΡΡΠΈΠΉ Π΄ΠΎΠ½ΠΎΡ Π³ΡΠΌΠ°Π½ΡΡΠ°ΡΠ½ΠΎΡ Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΈΡΠ΅ΡΠ΅Π΄ Π±Π°Π½ΠΊΡΠ² (Π§Π΅ΡΠ²ΠΎΠ½ΠΈΠΉ Π₯ΡΠ΅ΡΡ Π£ΠΊΡΠ°ΡΠ½ΠΈ, UNITED24, Superhumans, Π‘ΠΠΠΠΠΠ)β―
- ΠΠ΄ΠΈΠ½ ΡΠ· Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΈΡ ΠΏΠ»Π°ΡΠ½ΠΈΠΊΡΠ² ΠΏΠΎΠ΄Π°ΡΠΊΡΠ² Π² Π£ΠΊΡΠ°ΡΠ½Ρ, Π·Π° 2023 ΡΡΠΊ Π±ΡΠ»ΠΎ ΡΠΏΠ»Π°ΡΠ΅Π½ΠΎ 6,6 ΠΌΠ»ΡΠ΄ Π³ΡΠΈΠ²Π΅Π½Ρ
ΠΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ Π΄Π»Ρ Π²ΡΡΡ :β―
- Π Π°ΠΉΡ ΠΊΠ΅ΡΡΡΡΡΡΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°ΠΌΠΈ, ΡΠΎ ΡΠΎΠΊΡΡΡΡΡΡΡΡ Π½Π° Π»ΡΠ΄ΠΈΠ½Ρ ΡΠ° ΡΡ ΡΠΎΠ·Π²ΠΈΡΠΊΡ, Ρ ΡΠ΅Π½ΡΡΡ ΡΠ²Π°Π³ΠΈ 5β―500 ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ² ΡΠ° ΠΏΠΎΠ½Π°Π΄ 2,7 ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ ΠΊΠ»ΡΡΠ½ΡΡΠ²β―β―
- ΠΡΠ΄ΡΡΠΈΠΌΡΡΠΌΠΎ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΈ ΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΡΡ, ΡΡΠ²Π½ΠΎΡΡΡ ΡΠ° ΡΠ½ΠΊΠ»ΡΠ·ΠΈΠ²Π½ΠΎΡΡΡ
- ΠΠΈ Π²ΡΠ΄ΠΊΡΠΈΡΡ Π΄ΠΎ Π½Π°ΠΉΠΌΡ Π²Π΅ΡΠ΅ΡΠ°Π½ΡΠ² Ρ Π»ΡΠ΄Π΅ΠΉ Π· ΡΠ½Π²Π°Π»ΡΠ΄Π½ΡΡΡΡ ΡΠ° Π³ΠΎΡΠΎΠ²Ρ Π°Π΄Π°ΠΏΡΡΠ²Π°ΡΠΈ ΡΠΎΠ±ΠΎΡΠ΅ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ ΠΏΡΠ΄ Π²Π°ΡΡ ΠΎΡΠΎΠ±Π»ΠΈΠ²Ρ ΠΏΠΎΡΡΠ΅Π±ΠΈ
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡΡΠΌΠΎ Π·Ρ ΡΡΡΠ΄Π΅Π½ΡΠ°ΠΌΠΈ ΡΠ° Π»ΡΠ΄ΡΠΌΠΈ ΡΡΠ°ΡΡΠΎΠ³ΠΎ Π²ΡΠΊΡ,β―ΡΡΠ²ΠΎΡΡΡΡΠΈ ΡΠΌΠΎΠ²ΠΈ Π΄Π»Ρ Π·ΡΠΎΡΡΠ°Π½Π½Ρ Π½Π° Π±ΡΠ΄Ρ-ΡΠΊΠΎΠΌΡ Π΅ΡΠ°ΠΏΡ ΠΊΠ°ΡβΡΡΠΈ
ΠΠ°ΠΆΠ°ΡΡ Π΄ΡΠ·Π½Π°ΡΠΈΡΡ Π±ΡΠ»ΡΡΠ΅? β ΠΡΠ΄ΠΏΠΈΡΡΠΉΡΡ Π½Π° Π½Π°Ρ Ρ ΡΠΎΡ.ΠΌΠ΅ΡΠ΅ΠΆΠ°Ρ :
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Β· 50 views Β· 2 applications Β· 8d
Data Engineer
Office Work Β· Ukraine (Kyiv) Β· Product Β· 5 years of experience Β· English - B2About Us: Atto Trading, a dynamic quantitative trading firm founded in 2010 and leading in global high-frequency strategies, is looking for a Data Engineer to join our team. We are expanding an international, diverse team with experts in trading,...About Us:
Atto Trading, a dynamic quantitative trading firm founded in 2010 and leading in global high-frequency strategies, is looking for a Data Engineer to join our team.
We are expanding an international, diverse team with experts in trading, statistics, engineering, and technology. Our disciplined approach, combined with rapid market feedback, allows us to quickly turn ideas into profit. Our environment of learning and collaboration allows us to solve some of the worldβs hardest problems, together. As a small firm, we remain nimble and hold ourselves to the highest standards of integrity, ingenuity, and effort.
Role Highlights:
We are seeking an experienced Senior Data Engineer to design, build, and maintain our comprehensive Data Lake for a fast-growing number of research and production datasets. This role combines hardware and platform infrastructure expertise with data engineering excellence to support our rapidly growing data assets (~200TB current, scaling ~100TB/year).
Responsibilities:
- Architect and manage high-performance, scalable on-premise data storage systems optimized for large-scale data access and analytics workloads
- Configure and maintain compute clusters for distributed data processing
- Plan capacity and scalability roadmaps to accommodate 100TB+ annual data growth
- Design and implement efficient monitoring and alerting systems to forecast growth trends and proactively react to critical states
- Design, create, automate, and maintain various data pipelines
- Enhance existing and setup new βdata checksβ and alerts to determine when the data is βbadβ
- Design and implement a comprehensive on-premise Data Lake system connected to VAST storage solution for normalized market data across:
- US Equities, US Futures, and SIP feeds
- Other market data sources that will be further added
- Security Definition data for various markets
- Various private column data
- Build and operate endβtoβend data pipelines and SLA/SLO monitoring to ensure data quality, completeness, and governance
- Analyze existing data models, usage patterns, and access frequencies to identify bottlenecks and optimization opportunities
- Develop metadata and catalog layers for efficient data discovery and selfβservice access
- Design and deploy eventβdriven architectures for near realβtime market data processing and delivery
- Orchestrate ETL/ELT data pipelines using tools like Prefect (or Airflow), ensuring robustness, observability, and clear operational ownership
- Ensure fault tolerance, scalability, and high availability across existing systems
- Partner with traders, quantitative researchers, and other stakeholders to understand use cases and continuously improve the usability, performance, and reliability of the Data Lake
Requirements:- 5+ years of experience in data engineering or data platform roles
- Proven experience with largeβscale data infrastructure (hundreds of TBs of data, highβthroughput pipelines)
- Strong understanding of market data formats and financial data structures (e.g., trades, quotes, order books, corporate actions)
- Experience designing and modernizing data infrastructure within on-premise solutions
- Bachelorβs degree in Computer Science, Engineering or related field required; Masterβs degree preferred or equivalent practical experience
Tech Skills:- Data Engineering - Spark, Iceberg (or similar table formats), Trino/Presto, Parquet optimization
- ETL pipelines - Prefect/Airflow or similar DAG-oriented tools
- Infrastructure - High-performance networking and compute
- Storage Systems - High-performance distributed storage, NAS/SAN, object storage
- Networking - Low-latency networking (aware about DPDK and kernel bypass technologies. Data center infrastructure basics
- Programming - Python (productionβgrade), SQL, building APIs (e.g., FastAPI)
- Data Analysis - Advanced SQL, Tableau (or similar BI tools), data profiling tools
Nice to have:
- Experience in HFT or financial services
- Background in highβfrequency trading (HFT) or quantitative finance
What we offer:
- Competitive compensation package
- Performance-based bonus opportunities
- Healthcare & Sports/gym budget
- Mental health support, including access to therapy
- Paid time off (25 days)
- Relocation support (where applicable)
- International team meet-ups
- Learning and development support, including courses and certifications
- Access to professional tools, software, and resources
- Fully equipped workstations with high-quality hardware
- Modern office with paid lunches
Our motivation:
We are a company committed to staying at the forefront of technology. Our team is passionate about continual learning and improvement. With no external investors or customers, we are the primary users of the products we create, giving you the opportunity to make a real impact on our company's growth.
Ready to advance your career? Join our innovative team and help shape the future of trading on a global scale. Apply now and let's create the future together!
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Β· 14 views Β· 1 application Β· 4d
Cloud DevOps Engineer
Hybrid Remote Β· Ukraine Β· 4 years of experience Β· English - B2Hello everyone At Intobi, we're a software and product development company passionate about driving innovation and progress. We help our clients succeed by delivering custom-built tech solutions designed to meet their unique needs. Our expertise lies in...Hello everyone π
At Intobi, we're a software and product development company passionate about driving innovation and progress.
We help our clients succeed by delivering custom-built tech solutions designed to meet their unique needs.
Our expertise lies in developing cutting-edge Web and Mobile applications.
Weβre hiring a Cloud DevOps Engineer to drive the design, automation, and reliability of our multi-cloud infrastructure. This is a key role in a fast-paced startup environment, where youβll play a critical part in building, managing, and securing our cloud-native platform across AWS, Azure, and GCP.
Cyngular is an Israeli cybersecurity company focused on cloud investigation and automated incident response. The platform helps security teams detect, investigate, and respond to complex threats across AWS, Azure, and GCP
Role Overview:
As a Cloud DevOps Engineer, you will be responsible for implementing CI/CD pipelines, managing infrastructure as code, automating cloud operations, and ensuring high availability and security across environments. Youβll work closely with development, security, and data teams to enable fast, reliable, and secure deployments.
Key Responsibilities:
β Design, build, and maintain infrastructure using Terraform, CloudFormation, or Bicep.
β Manage CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps, etc.) across multiple cloud platforms.
β Automate provisioning and scaling of compute, storage, and networking resources in AWS, Azure, and GCP.
β Implement and maintain monitoring, logging, and alerting solutions (CloudWatch, Stackdriver, Azure Monitor, etc.).
β Harden environments according to security best practices (IAM, service principals, KMS, firewall rules, etc.).
β Support cost optimization strategies and resource tagging/governance.
β Collaborate with engineers to streamline developer workflows and cloud-based deployments.
Required Skills:
β 4+ years of experience in DevOps, Site Reliability Engineering, or Cloud Engineering.
β Hands-on experience with at least two major cloud providers (AWS, Azure, GCP); familiarity with the third.
β Proficiency in infrastructure as code (Terraform required; CloudFormation/Bicep is a plus).
β Experience managing containers and orchestration platforms (EKS, AKS, GKE, or Kubernetes).
β Strong knowledge of CI/CD tooling and best practices.
β Familiarity with secrets management, role-based access controls, and audit logging.
β Proficiency in scripting with Python, Bash, or PowerShell.
The position requires a high level of English β reading, writing, and speaking.
This role is not suitable for juniors or those with little to no experience.
Weβre looking for professional DevOps engineers who are passionate about technology and tools, and who arenβt afraid to take on significant responsibilities, including self-directed learning.
Please send your CV here or via email
Should the first stage be successfully completed, youβll be invited to a personal interview.
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Β· 4 views Β· 0 applications Β· 1h
Azure Data Engineer (ETL Developer)
Hybrid Remote Β· Ukraine Β· Product Β· 3 years of experience Β· English - B1Kyivstar is looking for Azure Data Engineer (Developer) to drive different life cycles of large systems. The Data Lifecycle Engineer will have opportunity to help customers realize their full potential through accelerated adoption and productive use of...Kyivstar is looking for Azure Data Engineer (Developer) to drive different life cycles of large systems. The Data Lifecycle Engineer will have opportunity to help customers realize their full potential through accelerated adoption and productive use of Microsoft Data and AI technologies.
Requirements:
β 3+ years of technical expertise in Database development (preferably with SQL, including Azure SQL) β designing and building database solutions (tables / stored procedures / forms / queries / etc.);
β Business intelligence knowledge with a deep understanding of data structure / data models to design and tune BI solutions;
β Advanced data analytics β designing and building solutions using technologies such as Databricks, Azure Data Factory, Azure Data Lake, HD Insights, SQL DW, stream analytics, machine learning, R server;
β Data formats knowledge and the differences between them;
β Experience with Hadoop stack;
β Experience with RDBMS and/or NoSQL;
β Experience with Kafka;
β Experience with Java and/or Scala and/or Python;
β Knowledge of version control system: git or bitbucket;
β BI Tools experience (PowerBI);
β Background in test driven development, automated testing and other software engineering best practices (e.g., performance, security, BDD, etc.);
β Docker/Kubernetes paradigm understanding;
β English β strong intermediate;
β Microsoft Certified is a plus.
Responsibility:
β Developing ETL flows based on Azure Cloud stack technology: such as Databricks, Azure Data Factory, Azure Data Lake, HD Insights, SQL DW, stream analytics, machine learning, R server;
β Troubleshooting and performance optimization for data processing flows, data models;
β Build and maintain reporting, models, dashboards.
We offer:
β A unique experience of working the most customers beloved and largest mobile operator in Ukraine;
β Real opportunity to ship digital products to millions of customers;
β To contribute into building the biggest analytical cloud environment in Ukraine;
β To create Big Data/AI products, changing the whole industry and influencing Ukraine;
β To be involved in real Big Data projects with Petabytes of data and Billions of events daily processed in Real-time;
β A competitive salary;
β Great possibilities for professional development and career growth;
β Medical insurance;
β Life insurance;
β Friendly & Collaborative Environment.
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Β· 384 views Β· 23 applications Β· 1d
Data Quality Engineer (ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΡΠΊΠΎΡΡΡ Π΄Π°Π½ΠΈΡ data-ΠΏΡΠΎΠ΄ΡΠΊΡΡ) State Statistics Service of Ukraine
Hybrid Remote Β· Ukraine Β· Product Β· 3 years of experience Β· English - B1ΠΠ΅ΡΠΆΠ°Π²Π½Π° ΡΠ»ΡΠΆΠ±Π° ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ Π£ΠΊΡΠ°ΡΠ½ΠΈ β ΡΠ΅ ΠΊΠΎΠΌΠ°Π½Π΄Π°, ΡΠΊΠ° ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΡΡ Π΄Π°Π½Ρ Π½Π° ΡΡΡΠ΅Π½Π½Ρ Π΄Π»Ρ ΡΠΎΠ·Π²ΠΈΡΠΊΡ ΠΊΡΠ°ΡΠ½ΠΈ. ΠΠΈ ΠΏΠ΅ΡΠ΅Π±ΡΠ²Π°ΡΠΌΠΎ Ρ ΠΏΡΠΎΡΠ΅ΡΡ ΡΠΈΡΡΠΎΠ²ΠΎΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ: Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΡΡΠΌΠΎ ΠΏΡΠΎΡΠ΅ΡΠΈ, Π±ΡΠ΄ΡΡΠΌΠΎ ΡΡΡΠ°ΡΠ½Ρ ΠΠ’-ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ ΡΠ° Π·Π°ΠΏΡΡΠΊΠ°ΡΠΌΠΎ ΡΠ΅ΡΠ²ΡΡΠΈ, ΡΠΎ ΡΠΎΠ±Π»ΡΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΡ...ΠΠ΅ΡΠΆΠ°Π²Π½Π° ΡΠ»ΡΠΆΠ±Π° ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ Π£ΠΊΡΠ°ΡΠ½ΠΈ β ΡΠ΅ ΠΊΠΎΠΌΠ°Π½Π΄Π°, ΡΠΊΠ° ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΡΡ Π΄Π°Π½Ρ Π½Π° ΡΡΡΠ΅Π½Π½Ρ Π΄Π»Ρ ΡΠΎΠ·Π²ΠΈΡΠΊΡ ΠΊΡΠ°ΡΠ½ΠΈ.
ΠΠΈ ΠΏΠ΅ΡΠ΅Π±ΡΠ²Π°ΡΠΌΠΎ Ρ ΠΏΡΠΎΡΠ΅ΡΡ ΡΠΈΡΡΠΎΠ²ΠΎΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ: Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΡΡΠΌΠΎ ΠΏΡΠΎΡΠ΅ΡΠΈ, Π±ΡΠ΄ΡΡΠΌΠΎ ΡΡΡΠ°ΡΠ½Ρ ΠΠ’-ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ ΡΠ° Π·Π°ΠΏΡΡΠΊΠ°ΡΠΌΠΎ ΡΠ΅ΡΠ²ΡΡΠΈ, ΡΠΎ ΡΠΎΠ±Π»ΡΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΡ Π·ΡΠΎΠ·ΡΠΌΡΠ»ΠΎΡ ΠΉ Π΄ΠΎΡΡΡΠΏΠ½ΠΎΡ Π΄Π»Ρ Π²ΡΡΡ . Π‘ΡΠΎΠ³ΠΎΠ΄Π½Ρ ΠΌΠΈ ΠΏΠΎΡΠΈΠ»ΡΡΠΌΠΎ ΠΠ’-Π½Π°ΠΏΡΡΠΌ Ρ ΡΡΠΊΠ°ΡΠΌΠΎ ΡΠ°Ρ ΡΠ²ΡΡΠ², ΡΠΊΡ Ρ ΠΎΡΡΡΡ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΡΠ²Π°ΡΠΈ Π½ΠΎΠ²Ρ ΠΏΡΠ΄Ρ ΠΎΠ΄ΠΈ ΡΠ° ΡΡΠ²ΠΎΡΡΠ²Π°ΡΠΈ ΡΡΡΠ°ΡΠ½Ρ Π΄Π΅ΡΠΆΠ°Π²Π½Ρ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΡ ΡΠ°Π·ΠΎΠΌ ΡΠ· Π½Π°ΡΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ.
Π£ ΠΠ΅ΡΠΆΡΡΠ°ΡΡ Π²Π°ΡΠ° ΡΠΎΠ±ΠΎΡΠ° Π²ΠΏΠ»ΠΈΠ²Π°Ρ Π½Π° Π±ΡΠ»ΡΡΠ΅, Π½ΡΠΆ ΠΎΠ΄Π½Ρ ΡΠΈΡΡΠ΅ΠΌΡ β Π²ΠΎΠ½Π° Π·ΠΌΡΠ½ΡΡ Π΄Π΅ΡΠΆΠ°Π²Π½Ρ ΡΠ΅ΡΠ²ΡΡΠΈ ΡΠ° Π·ΠΌΡΡΠ½ΡΡ Π΄ΠΎΠ²ΡΡΡ Π³ΡΠΎΠΌΠ°Π΄ΡΠ½ Π΄ΠΎ Π΄Π΅ΡΠΆΠ°Π²ΠΈ
ΠΠ°Ρ ΡΠ΄Π΅Π°Π»ΡΠ½ΠΈΠΉ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ (ΠΊΠ°) β Π΄ΠΎΡΠ²ΡΠ΄ΡΠ΅Π½Π° ΡΠ° Π΅Π½Π΅ΡΠ³ΡΠΉΠ½Π° ΠΎΡΠΎΠ±Π°, ΡΠΊΠ° ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈΠΌΠ΅ ΡΠ΅ΡΡΡΠ²Π°Π»ΡΠ½ΠΈΠΊΠΎΠΌ (ΡΠ΅Ρ) ΡΠΊΠΎΡΡΡ Π΄Π°ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² Π² Π¦Π΅Π½ΡΡΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ . ΠΠΈ Π³ΡΠ°ΡΠΈΠΌΠ΅ΡΠ΅ ΠΊΠ»ΡΡΠΎΠ²Ρ ΡΠΎΠ»Ρ Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ² ΡΠΊΡΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ!
ΠΠ±ΠΎΠ²ΚΌΡΠ·ΠΊΠΈ:- ΠΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡ Π·Π°Π΄Π»Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎΡΡΡ Π²ΠΈΠΌΠΎΠ³Π°ΠΌ Ρ Π½Π°ΡΠ²Π½ΠΈΠΌ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌ.
- Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΡΡΠ°ΡΠ΅Π³ΡΡ QA Π΄Π»Ρ ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡ.
- Π’Π΅ΡΡΡΠ²Π°Π½Π½Ρ Π΄Π°ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² Ρ Π΄Π°ΡΠ° ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ.
- Π’Π΅ΡΡΠΎΠ²Π° ΠΊΠΎΠ½ΡΡΠ³ΡΡΠ°ΡΡΡ Π΄Π°ΡΠ° ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ.
- ΠΠ΅ΡΠ΅Π²ΡΡΠΊΠ° ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΡ ΡΠ° ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² Π½Π° Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΡΡΡΡ Π²ΠΈΠΌΠΎΠ³Π°ΠΌ Ρ ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΡΡ Π·Π°Π΄Π°ΡΡ.
- ΠΠΎΠΊΡΠΌΠ΅Π½ΡΡΠ²Π°Π½Π½Ρ ΠΏΠΎΠΌΠΈΠ»ΠΎΠΊ Ρ ΡΡ ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠ° ΡΠ° Π²ΠΈΠΏΡΠ°Π²Π»Π΅Π½Π½Ρ.
- ΠΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Ρ Π·Π° ΡΠΊΡΡΡΡ ΠΊΡΠ½ΡΠ΅Π²ΠΈΡ
ΡΠ° ΠΎΠ±ΡΠΎΠ±Π»Π΅Π½ΠΈΡ
Π΄Π°Π½ΠΈΡ
, ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ Π΄Π°ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡΡ.
Π§Π΅ΠΊΠ°ΡΠΌΠΎ Π²ΡΠ΄ Π²Π°Ρ:
- ΠΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° Π² Π³Π°Π»ΡΠ·Ρ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈ, ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ Π°Π±ΠΎ ΡΡΠΌΡΠΆΠ½ΠΈΡ Π³Π°Π»ΡΠ·Π΅ΠΉ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Ρ ΡΡΠ΅ΡΡ IT Π½Π° ΠΏΠΎΠ·ΠΈΡΡΡ QA.
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ΠΠΎΠ΄Π°ΡΠΊΠΎΠ²Ρ Π½Π°Π²ΠΈΡΠΊΠΈ: (Will be a plus)- ΠΠΎΡΠ²ΡΠ΄ BI ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ, ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ETL-ΠΏΡΠΎΡΠ΅ΡΡΠ²
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- Jira ΡΠ° Confluence (ΡΠΈ ΡΠ½ΡΠΈΠΉ ΡΠ°ΡΠΊ ΡΡΠ΅ΠΊΠ΅Ρ), Miro (ΡΠΈ ΡΠ½ΡΠΈΠΉ Π²Π°ΠΉΡΠ±ΠΎΡΠ΄ Π΄Π»Ρ ΠΊΠΎΠ»Π΅ΠΊΡΠΈΠ²Π½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ ΠΎΠ½Π»Π°ΠΉΠ½).
- Π’Π°ΠΉΠΌ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½Ρ ΡΠ° ΠΏΡΡΠΎΡΠΈΡΠΈΠ·Π°ΡΡΡ.
Π€ΠΎΡΠΌΠ°Ρ ΡΠΎΠ±ΠΎΡΠΈ:Π ΠΎΠ±ΠΎΡΠΈΠΉ Π΄Π΅Π½Ρ 08:00β17:00 (Π· ΠΎΠ±ΡΠ΄Π½ΡΠΎΡ ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡ).
Π ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΠ°ΡΡΠΊΠΎΠ²ΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ ΠΎΠ½Π»Π°ΠΉΠ½, Π°Π»Π΅ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ Π±ΡΡΠΈ Π΄ΠΎΡΡΡΠΏΠ½ΠΈΠΌ Ρ ΠΠΈΡΠ²Ρ ΠΏΡΠΈ ΠΏΠΎΡΡΠ΅Π±Ρ.
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:- ΠΎΡΡΡΡΠΉΠ½Π΅ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ Π·Π° ΠΠΠΏΠ Ρ ΠΏΠΎΠ²Π½ΠΈΠΉ ΡΠΎΡΠΏΠ°ΠΊΠ΅Ρ (24 Π΄Π½Ρ Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ, ΠΎΠΏΠ»Π°ΡΡΠ²Π°Π½Ρ Π»ΡΠΊΠ°ΡΠ½ΡΠ½Ρ);
- ΡΡΠ°Π±ΡΠ»ΡΠ½ΡΡΡΡ Ρ ΠΏΡΠΎΠ·ΠΎΡΡ ΡΠΌΠΎΠ²ΠΈ;
- ΡΡΠ°ΡΡΡ Ρ ΠΏΡΠΎΡΠΊΡΠ°Ρ , ΡΠΎ Π²ΠΏΠ»ΠΈΠ²Π°ΡΡΡ Π½Π° ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ ΠΊΡΠ°ΡΠ½ΠΈ ΡΠ° Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΡΡ Π»ΡΠ΄ΡΠΌ;
- ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ Π΄Π»Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΎΠ³ΠΎ Π·ΡΠΎΡΡΠ°Π½Π½Ρ ΠΉ Π½Π°Π²ΡΠ°Π½Π½Ρ;
- ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΡ Π²Π°ΡΠΈΡ
ΡΠ΄Π΅ΠΉ ΡΠ° ΡΠ½ΡΡΡΠ°ΡΠΈΠ².
ΠΡΠΎΡΠ΅Ρ Π½Π°ΠΉΠΌΡ
ΠΠΈ ΡΡΠ½ΡΡΠΌΠΎ Π²ΡΠ΄ΠΊΡΠΈΡΡΡΡΡ Ρ Ρ ΠΎΡΠ΅ΠΌΠΎ, ΡΠΎΠ± Π²ΠΈ Π·Π½Π°Π»ΠΈ, ΡΠΊ ΠΏΡΠΎΡ ΠΎΠ΄ΠΈΡΡ Π½Π°Ρ ΠΏΡΠΎΡΠ΅Ρ Π²ΡΠ΄Π±ΠΎΡΡ β ΠΏΡΠΎΠ·ΠΎΡΠΎ, Π·ΡΠΎΠ·ΡΠΌΡΠ»ΠΎ ΡΠ° Π· ΠΏΠΎΠ²Π°Π³ΠΎΡ Π΄ΠΎ Π²Π°ΡΠΎΠ³ΠΎ ΡΠ°ΡΡ. ΠΠ°Π·Π²ΠΈΡΠ°ΠΉ Π²ΡΠ½ ΡΠΊΠ»Π°Π΄Π°ΡΡΡΡΡ Π· ΠΊΡΠ»ΡΠΊΠΎΡ Π΅ΡΠ°ΠΏΡΠ² Ρ ΡΡΠΈΠ²Π°Ρ Π²ΡΠ΄ ΠΊΡΠ»ΡΠΊΠΎΡ Π΄Π½ΡΠ² Π΄ΠΎ Π΄Π²ΠΎΡ ΡΠΈΠΆΠ½ΡΠ², Π·Π°Π»Π΅ΠΆΠ½ΠΎ Π²ΡΠ΄ ΡΠΎΠ»Ρ.
ΠΠΈ Π·Π°Π²ΠΆΠ΄ΠΈ Π½Π°Π΄Π°ΡΠΌΠΎ ΡΡΠ΄Π±Π΅ΠΊ ΠΏΡΡΠ»Ρ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Π΅ΡΠ°ΠΏΡ, ΡΠΎΠ± Π²ΠΈ Π·Π½Π°Π»ΠΈ, Π½Π° ΡΠΊΠΎΠΌΡ Π΅ΡΠ°ΠΏΡ ΠΏΠ΅ΡΠ΅Π±ΡΠ²Π°ΡΡΠ΅.
- ΠΠ΅ΡΠ²ΠΈΠ½Π½Π° ΡΠΏΡΠ²Π±Π΅ΡΡΠ΄Π°.
ΠΠ΅ΡΠ΅Π²ΡΡΠΊΠ° ΡΠ΅Π·ΡΠΌΠ΅ ΡΠ° Π±Π°Π·ΠΎΠ²ΠΈΡ Π½Π°Π²ΠΈΡΠΎΠΊ. ΠΠ±Π³ΠΎΠ²ΠΎΡΠ΅Π½Π½Ρ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ±ΠΎΡΠΈ, ΠΌΠΎΡΠΈΠ²Π°ΡΡΡ ΡΠ° ΠΎΡΡΠΊΡΠ²Π°Π½Ρ. Π ΠΎΠ·ΠΏΠΎΠ²ΡΠ΄Ρ ΠΏΡΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΠΊΠΎΠΌΠ°Π½Π΄Ρ ΡΠ° ΡΠΌΠΎΠ²ΠΈ ΡΠΎΠ±ΠΎΡΠΈ. - Π’Π΅Ρ
Π½ΡΡΠ½Π° ΡΠΏΡΠ²Π±Π΅ΡΡΠ΄Π°.
ΠΠΈΡΠ°Π½Π½Ρ ΠΏΠΎ ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ·Π°ΡΡΡ (ΡΠ΅ΠΎΡΡΡ + ΠΏΡΠ°ΠΊΡΠΈΡΠ½Ρ ΠΏΡΠΈΠΊΠ»Π°Π΄ΠΈ). ΠΠ±Π³ΠΎΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΏΠΎΠΏΠ΅ΡΠ΅Π΄Π½ΡΡ ΠΏΡΠΎΡΠΊΡΡΠ². ΠΠΎΠΆΠ»ΠΈΠ²Π΅ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ Π½Π΅Π²Π΅Π»ΠΈΠΊΠΎΠ³ΠΎ Π·Π°Π²Π΄Π°Π½Π½Ρ Π² ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΡΡ. - Π’Π΅ΡΡΠΎΠ²Π΅ Π·Π°Π²Π΄Π°Π½Π½Ρ (Π·Π° ΠΏΠΎΡΡΠ΅Π±ΠΈ).
ΠΠ°Π΄Π°Π½Π½Ρ Π·Π°Π²Π΄Π°Π½Π½Ρ Π· ΡΡΡΠΊΠΈΠΌΠΈ ΡΠ½ΡΡΡΡΠΊΡΡΡΠΌΠΈ ΡΠ° Π΄Π΅Π΄Π»Π°ΠΉΠ½ΠΎΠΌ. ΠΠ΅ΡΠ΅Π²ΡΡΠΊΠ° ΡΠΊΠΎΡΡΡ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ, ΠΊΡΠ΅Π°ΡΠΈΠ²Π½ΠΎΡΡΡ, Π΄ΠΎΡΡΠΈΠΌΠ°Π½Π½Ρ Π²ΠΈΠΌΠΎΠ³. - Π€ΡΠ½Π°Π»ΡΠ·Π°ΡΡΡ Π½Π°ΠΉΠΌΡ.
ΠΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° ΡΠ° Π²ΡΠ΄ΠΏΡΠ°Π²ΠΊΠ° ΠΎΡΠ΅ΡΡ (ΡΠΌΠΎΠ²ΠΈ ΡΠΎΠ±ΠΎΡΠΈ, Π·Π°ΡΠΏΠ»Π°ΡΠ°, Π΄Π°ΡΠ° Π²ΠΈΡ ΠΎΠ΄Ρ). ΠΠ±Π³ΠΎΠ²ΠΎΡΠ΅Π½Π½Ρ Π΄Π΅ΡΠ°Π»Π΅ΠΉ ΡΠ° Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Ρ Π½Π° Π·Π°ΠΏΠΈΡΠ°Π½Π½Ρ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ°. ΠΡΡΠΊΡΠ²Π°Π½Π½Ρ ΠΏΡΠ΄ΡΠ²Π΅ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ° ΠΏΡΠ΄ΠΏΠΈΡΠ°Π½Π½Ρ ΠΎΡΠ΅ΡΡ.
ΠΠ°Ρ Π΄ΠΎΡΠ²ΡΠ΄ β Π½Π°Ρ Π½Π°ΡΡΡΠΏΠ½ΠΈΠΉ ΠΊΡΠΎΠΊ Π΄ΠΎ ΡΡΠΏΡΡ Ρ.
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Π―ΠΊΡΠΎ Π²ΠΈ Π±Π°ΡΠΈΡΠ΅ ΡΠ΅Π±Π΅ ΡΠ°ΡΡΠΈΠ½ΠΎΡ Π½Π°ΡΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, Π½Π΅ Π·Π²ΠΎΠ»ΡΠΊΠ°ΠΉΡΠ΅ Π· Π²ΡΠ΄Π³ΡΠΊΠΎΠΌ.
Π§Π΅ΠΊΠ°ΡΠΌΠΎ Π½Π° Π²Π°ΡΠ΅ ΡΠ΅Π·ΡΠΌΠ΅ Π΄Π»Ρ Π·Π½Π°ΠΉΠΎΠΌΡΡΠ²Π° ΡΠ° ΠΎΠ±Π³ΠΎΠ²ΠΎΡΠ΅Π½Π½Ρ Π΄Π΅ΡΠ°Π»Π΅ΠΉ ΡΠΏΡΠ²ΠΏΡΠ°ΡΡ. -
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Junior Data Engineer
Hybrid Remote Β· Ukraine Β· Product Β· 2 years of experience Β· English - None Ukrainian Product πΊπ¦TENTENS Tech Ρ ΠΏΠ°ΡΡΠ½Π΅ΡΡΡΠΊΠΎΡ IT-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡΡ SKELAR β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΎΠ³ΠΎ Π²Π΅Π½ΡΡΡ-Π±ΡΠ»Π΄Π΅ΡΠ°, ΡΠΊΠΈΠΉ Π±ΡΠ΄ΡΡ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ tech-Π±ΡΠ·Π½Π΅ΡΠΈ. ΠΠΈ ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡΠΌΠΎ social discovery-ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ Π΄Π»Ρ ΠΏΠΎΠ½Π°Π΄ 20 ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ² Ρ Π²ΡΡΠΎΠΌΡ ΡΠ²ΡΡΡ. ΠΠ°Ρ ΡΠ΅ΠΌΠΏ Ρ ΠΌΠ°ΡΡΡΠ°Π± ΡΡΠΈΠΌΠ°ΡΡΡΡΡ Π½Π° Π³Π»ΠΈΠ±ΠΎΠΊΡΠΉ...TENTENS Tech Ρ ΠΏΠ°ΡΡΠ½Π΅ΡΡΡΠΊΠΎΡ IT-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡΡ SKELAR β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΎΠ³ΠΎ Π²Π΅Π½ΡΡΡ-Π±ΡΠ»Π΄Π΅ΡΠ°, ΡΠΊΠΈΠΉ Π±ΡΠ΄ΡΡ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ tech-Π±ΡΠ·Π½Π΅ΡΠΈ.
ΠΠΈ ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡΠΌΠΎ social discovery-ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ Π΄Π»Ρ ΠΏΠΎΠ½Π°Π΄ 20 ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ² Ρ Π²ΡΡΠΎΠΌΡ ΡΠ²ΡΡΡ. ΠΠ°Ρ ΡΠ΅ΠΌΠΏ Ρ ΠΌΠ°ΡΡΡΠ°Π± ΡΡΠΈΠΌΠ°ΡΡΡΡΡ Π½Π° Π³Π»ΠΈΠ±ΠΎΠΊΡΠΉ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·Ρ Π² Π°Π½Π°Π»ΡΡΠΈΡΡ, highload ΡΠ° performance marketing.ΠΠΎΠΌΠ°Π½Π΄Π° TENTENS Tech ΡΡΠΎΠ³ΠΎΠ΄Π½Ρ β ΡΠ΅ 250+ Π»ΡΠ΄Π΅ΠΉ, Π΄Π΅ ΠΊΠΎΠΆΠ΅Π½ Π΄ΡΡ ΡΠΊ CEO Ρ ΡΠ²ΠΎΡΠΉ ΡΠΎΠ»Ρ ΡΠ° Π²ΠΏΠ»ΠΈΠ²Π°Ρ Π½Π° ΡΠΏΡΠ»ΡΠ½ΠΈΠΉ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ. ΠΡΠ°ΡΡΡΠΌΠΎ Π² ΡΡΠΈΠ»Ρ 10/10ths: ΡΡΠΈΠΌΠ°ΡΠΌΠΎ ΠΊΡΡΡ Π½Π° ΡΡΠ»Ρ, ΡΡΠ°Π²ΠΈΠΌΠΎ ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½Ρ ΠΏΠΈΡΠ°Π½Π½Ρ Π½Π° ΡΠ»ΡΡ Ρ Ρ Π½Π΅ Π·ΡΠΏΠΈΠ½ΡΡΠΌΠΎΡΡ Π½Π° Β«Π΄ΠΎΠ±ΡΠ΅Β» β ΡΡΠ»ΡΠΊΠΈ ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌ Ρ ΡΠ΅ ΡΡΠΎΡ ΠΈ Π΄Π°Π»Ρ. Π―ΠΊΡΠΎ Π²ΠΏΡΠ·Π½Π°ΡΡ ΡΠ΅Π±Π΅ Π² ΡΡΠΎΠΌΡ ΠΎΠΏΠΈΡΡ ΡΠ° Π³ΠΎΡΠΎΠ²ΠΈΠΉ Π½Π°Π±ΠΈΡΠ°ΡΠΈ ΡΠ²ΠΈΠ΄ΠΊΡΡΡΡ ΡΠ· Π½Π°ΠΌΠΈ β Π΄Π°Π²Π°ΠΉ Π·Π½Π°ΠΉΠΎΠΌΠΈΡΠΈΡΡ!
ΠΠ°ΡΠ°Π· ΠΌΠΈ Π² ΠΏΠΎΡΡΠΊΡ Junior Data Engineer, ΡΠΎΠ± ΡΠ°Π·ΠΎΠΌ ΠΏΡΠ΄Π½ΡΠΌΠ°ΡΠΈ ΠΏΠ»Π°Π½ΠΊΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ.
ΠΠ° ΡΡΠΉ ΠΏΠΎΠ·ΠΈΡΡΡ Π²ΠΈ ΠΏΡΠΈΡΠ΄Π½Π°ΡΡΠ΅ΡΡ Π΄ΠΎ Π½Π°ΡΠΎΡ data-ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ ΡΠ° ΠΏΡΠ΄ ΠΊΠ΅ΡΡΠ²Π½ΠΈΡΡΠ²ΠΎΠΌ Π΄ΠΎΡΠ²ΡΠ΄ΡΠ΅Π½ΠΈΡ ΡΠ½ΠΆΠ΅Π½Π΅ΡΡΠ² Π±ΡΠ΄Π΅ΡΠ΅ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΠΈ ΠΏΡΠ΄ΡΡΠΈΠΌΡΠ²Π°ΡΠΈ Ρ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΡ, ΡΠΎ Ρ ΠΎΡΠ½ΠΎΠ²ΠΎΡ Π΄Π»Ρ Π½Π°ΡΠΈΡ Π±ΡΠ·Π½Π΅Ρ-ΡΡΡΠ΅Π½Ρ, Π° ΡΠ°ΠΊΠΎΠΆ Π±ΡΠ΄Π΅ΡΠ΅:
- Π ΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ data-ΠΏΡΠΎΡΠ΅ΡΠΈ: Π±ΡΠ°ΡΠΈ ΡΡΠ°ΡΡΡ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΠΉ, ΡΠΊΡ Π·Π°Π²Π°Π½ΡΠ°ΠΆΡΡΡΡ ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ Π·Π°ΠΏΠΈΡΡΠ² ΡΠΎΠ³ΠΎΠ΄ΠΈΠ½ΠΈ.
- ΠΡΠ°ΡΡΠ²Π°ΡΠΈ Π· Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΎΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΎΡ: Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΠΈ Π² ΡΠΎΠ·ΡΠΎΠ±ΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π°Π½ΠΈΡ ΡΠ° ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΠΉ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ Dataform/DBT.
- ΠΡΠ΄ΡΡΠΈΠΌΡΠ²Π°ΡΠΈ Data Lake: Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΡΠ²Π°ΡΠΈ ΡΡΠ°Π±ΡΠ»ΡΠ½ΡΡΡΡ ΡΠ° Π΄ΠΎΡΡΡΠΏΠ½ΡΡΡΡ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π²ΡΡΡΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
- ΠΠΏΡΠΈΠΌΡΠ·ΡΠ²Π°ΡΠΈ ΠΎΠ±ΡΠΈΡΠ»Π΅Π½Π½Ρ: ΠΏΠΈΡΠ°ΡΠΈ ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΡΠ²Π°ΡΠΈ ΡΠΊΠ»Π°Π΄Π½Ρ Π°Π½Π°Π»ΡΡΠΈΡΠ½Ρ SQL-Π·Π°ΠΏΠΈΡΠΈ, ΡΠΊΡ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΡΡΡΡΡ Π΄Π»Ρ Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ ΡΠ° Π·Π²ΡΡΠ½ΠΎΡΡΡ.
Π©ΠΎ Π΄Π»Ρ Π½Π°Ρ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ:
ΠΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ°, ΡΠΊΠΈΠΉ ΠΌΠ°Ρ ΠΌΡΡΠ½Ρ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ½Ρ Π±Π°Π·Ρ ΡΠ° ΠΏΡΠ°Π³Π½Π΅ ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΠΈΡΠΈ ΡΡ Π½Π° ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Ρ ΡΠΎΠ±ΠΎΡΡ Π·Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΠΌΠΈ ΡΠ° Π²ΠΈΡΠΎΠΊΠΎΠ½Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½ΠΈΠΌΠΈ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ.
Must-have:
- Python: ΠΠΌΡΠ½Π½Ρ ΠΏΠΈΡΠ°ΡΠΈ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈΠΉ ΡΠ° ΡΠΈΡΡΠΈΠΉ ΠΊΠΎΠ΄ Π΄Π»Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ .
- SQL: ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ SQL Π΄Π»Ρ ΡΠΎΠ±ΠΎΡΠΈ Π· Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ , Π²ΠΊΠ»ΡΡΠ½ΠΎ Π· JOIN, GROUP BY ΡΠ° Π²ΡΠΊΠΎΠ½Π½ΠΈΠΌΠΈ ΡΡΠ½ΠΊΡΡΡΠΌΠΈ.
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΠΉ DE: ΠΠ°Π·ΠΎΠ²Ρ Π·Π½Π°Π½Π½Ρ ΠΏΡΠΎ Data Modelling, Data Patterns, ΡΠ° Π°ΡΡ
ΡΡΠ΅ΠΊΡΡΡΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ
ΡΡ
ΠΎΠ²ΠΈΡ (Π½Π°ΠΏΡ., BigQuery).
Nice-to-have:
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Ρ ΠΌΠ°ΡΠ½ΠΈΠΌΠΈ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ°ΠΌΠΈ, ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎ Π· GCP/BigQuery.
- Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ½Π΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ² ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΡΡ (Airflow) ΡΠ° ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ (Dataform/DBT).
Π Π³ΠΎΠ»ΠΎΠ²Π½Π΅ β ΡΠΈΡΠ° Π·Π°ΡΡΠΊΠ°Π²Π»Π΅Π½ΡΡΡΡ Ρ ΡΡΠ΅ΡΡ Π΄Π°Π½ΠΈΡ , Π΄ΠΎΠΏΠΈΡΠ»ΠΈΠ²ΡΡΡΡ ΡΠ° Π±Π°ΠΆΠ°Π½Π½Ρ ΡΠ²ΠΈΠ΄ΠΊΠΎ Π²ΡΠΈΡΠΈΡΡ Ρ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈΡΡ.
Π©ΠΎ ΡΠΈ ΠΎΡΡΠΈΠΌΠ°ΡΡ ΡΠ°Π·ΠΎΠΌ Π· Π½Π°ΠΌΠΈ:
β ΠΠ½ΡΡΡΡΡΠ½Ρ ΠΊΠ»ΡΠ±ΠΈ Π·Π° ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΠΌΠΈ Π½Π°ΠΏΡΡΠΌΠΊΠ°ΠΌΠΈ: ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³, ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ°, ΡΡΠ½Π°Π½ΡΠΈ, ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³;
β Π’ΡΠ΅Π½ΡΠ½Π³ΠΈ, ΠΊΡΡΡΠΈ, Π²ΡΠ΄Π²ΡΠ΄ΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΡΠΉ;
β ΠΡΠΎΡΡΠΎΡΡ ΡΠ° ΡΡΡΠ°ΡΠ½Ρ ΠΎΡΡΡΠΈ Ρ ΠΠΈΡΠ²Ρ ΡΠ° ΠΠ°ΡΡΠ°Π²Ρ;
β ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ, ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΈΠΉ Π»ΡΠΊΠ°Ρ.
ΠΠ°Π²Π°ΠΉ ΡΠ°Π·ΠΎΠΌ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ the next big everything β ΡΠ°ΠΌ, Π΄Π΅ Β«Π΄ΠΎΠ±ΡΠ΅Β» Π½Π΅ ΡΡΠ½ΡΡ, Π° ΡΡΠ°ΡΡ!
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Analytics Engineer (6037 / Genesis)
Hybrid Remote Β· Ukraine Β· Product Β· 3 years of experience Β· English - B1Π¨ΡΠΊΠ°ΡΠΌΠΎ Analytics Engineer-Π°, ΡΠΊΠΈΠΉ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΠΈΠΌΠ΅ Π·Π° Π΄ΠΎΡΡΠΎΠ²ΡΡΠ½ΡΡΡΡ Ρ ΡΡΠ»ΡΡΠ½ΡΡΡΡ Π΄Π°Π½ΠΈΡ Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎΠ± ΡΠΈ ΡΠ° ΡΠ½ΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ ΠΌΠΎΠ³Π»ΠΈ ΡΠ²ΠΈΠ΄ΠΊΠΎ Π·Π½Π°Ρ ΠΎΠ΄ΠΈΡΠΈ, ΡΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΡΠ²Π°ΡΠΈ ΠΉ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°ΡΠΈ ΠΏΠΎΡΡΡΠ±Π½Ρ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ Π±Π΅Π· Π΄ΡΠ±Π»ΡΠ²Π°Π½Π½Ρ Π»ΠΎΠ³ΡΠΊΠΈ ΡΠΈ ΡΡΡΠ½ΠΎΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ. Π£ ΡΡΠΉ ΡΠΎΠ»Ρ ΡΠΈ...Π¨ΡΠΊΠ°ΡΠΌΠΎ Analytics Engineer-Π°, ΡΠΊΠΈΠΉ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΠΈΠΌΠ΅ Π·Π° Π΄ΠΎΡΡΠΎΠ²ΡΡΠ½ΡΡΡΡ Ρ ΡΡΠ»ΡΡΠ½ΡΡΡΡ Π΄Π°Π½ΠΈΡ Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎΠ± ΡΠΈ ΡΠ° ΡΠ½ΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ ΠΌΠΎΠ³Π»ΠΈ ΡΠ²ΠΈΠ΄ΠΊΠΎ Π·Π½Π°Ρ ΠΎΠ΄ΠΈΡΠΈ, ΡΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΡΠ²Π°ΡΠΈ ΠΉ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°ΡΠΈ ΠΏΠΎΡΡΡΠ±Π½Ρ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ Π±Π΅Π· Π΄ΡΠ±Π»ΡΠ²Π°Π½Π½Ρ Π»ΠΎΠ³ΡΠΊΠΈ ΡΠΈ ΡΡΡΠ½ΠΎΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ.
Π£ ΡΡΠΉ ΡΠΎΠ»Ρ ΡΠΈ ΡΠΎΡΠΌΡΠ²Π°ΡΠΈΠΌΠ΅Ρ ΠΎΡΠ½ΠΎΠ²Ρ Π²ΡΡΡΡ Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ β Π²ΡΠ΄ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΡΡΡΡΠΊΡΡΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π°ΡΠ°-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄ΠΎ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠ°ΡΡΡΠ°Π±ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠ² ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΠΉ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ Π΄Π°Π½ΠΈΡ . Π’Π²ΠΎΡ ΡΠΎΠ±ΠΎΡΠ° Π½Π°ΠΏΡΡΠΌΡ Π²ΠΏΠ»ΠΈΠ²Π°ΡΠΈΠΌΠ΅ Π½Π° ΡΠΊΡΡΡΡ ΡΠΏΡΠ°Π²Π»ΡΠ½ΡΡΠΊΠΈΡ ΡΡΡΠ΅Π½Ρ, Π°Π½Π°Π»ΡΠ· ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ², Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ΠΎΠ²ΠΈΡ ΠΊΠ°ΠΌΠΏΠ°Π½ΡΠΉ Ρ ΡΠΎΡΠ½ΡΡΡΡ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡΠ².
Π¦Π΅ ΠΏΠΎΠ·ΠΈΡΡΡ Π· ΠΏΡΡΠΌΠΈΠΌ Π²ΠΏΠ»ΠΈΠ²ΠΎΠΌ Π½Π° ΠΌΠ°ΡΡΡΠ°Π±ΡΠ²Π°Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² ΡΠ° Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡΡΡ Π±ΡΠ·Π½Π΅ΡΡ. Π’ΠΈ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΡΠ²Π°ΡΠΈΠΌΠ΅Ρ ΡΡΠ°Π±ΡΠ»ΡΠ½Ρ ΠΎΡΠ½ΠΎΠ²Ρ, Π½Π° ΡΠΊΡΠΉ Π±ΡΠ΄ΡΡΡΡΡΡ Π²ΡΡ Π°Π½Π°Π»ΡΡΠΈΠΊΠ° ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, Ρ ΠΌΠ°ΡΠΈΠΌΠ΅Ρ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΠΎΡΠΌΡΠ²Π°ΡΠΈ ΡΡΠ°Π½Π΄Π°ΡΡΠΈ ΡΠΎΠ±ΠΎΡΠΈ Π· Π΄Π°Π½ΠΈΠΌΠΈ Π½Π° ΡΡΠ²Π½Ρ Π²ΡΡΡΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
ΠΠ΅ΡΠ΅Π²Π°Π³ΠΈ ΠΏΠΎΠ·ΠΈΡΡΡ:
- ΠΠΎΠ²Π½ΠΈΠΉ ownership Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΡΡ Π΄Π°Π½ΠΈΡ Π² DWH Π½Π° Π΅ΡΠ°ΠΏΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ ΡΠ° ΠΏΡΡΠΌΠΈΠΉ Π²ΠΏΠ»ΠΈΠ² Π½Π° ΠΌΠ°ΡΡΡΠ°Π±ΡΠ²Π°Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ².
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΡΠ°ΡΠΈ ΠΊΠ»ΡΡΠΎΠ²ΠΈΠΌ ΡΡΠ°ΡΠ½ΠΈΠΊΠΎΠΌ ΡΠΎΠ·Π²ΠΈΡΠΊΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΎΡ ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠΈ, ΠΎΡΠΊΡΠ»ΡΠΊΠΈ Π·Π°ΡΠ°Π· Ρ ΠΊΠΎΠΌΠ°Π½Π΄Ρ ΠΏΡΠ°ΡΡΡ ΠΎΠ΄ΠΈΠ½ Π΄Π°ΡΠ°-ΡΠ½ΠΆΠ΅Π½Π΅Ρ.
- Π ΠΎΠ±ΠΎΡΠ° Π· ΡΡΡΠ°ΡΠ½ΠΈΠΌ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΠΌ ΡΡΠ΅ΠΊΠΎΠΌ: BigQuery + dbt + AWS, Π° ΡΠ°ΠΊΠΎΠΆ Π²ΠΏΠ»ΠΈΠ² Π½Π° ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΡΠ΅Ρ Π½ΡΡΠ½ΠΈΡ ΡΡΠ°Π½Π΄Π°ΡΡΡΠ².
- Π’ΡΡΠ½Π° Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ Π· ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΈΠΌΠΈ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ ΡΠ° Π²ΠΈΠ΄ΠΈΠΌΠΈΠΉ Π²ΠΏΠ»ΠΈΠ² ΡΠ²ΠΎΡΡ ΡΡΡΠ΅Π½Ρ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²Ρ, ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ΠΎΠ²Ρ ΠΉ ΡΡΠ½Π°Π½ΡΠΎΠ²Ρ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡΡΡ.
Π’Π²ΠΎΡ ΠΌΠ°ΠΉΠ±ΡΡΠ½Ρ Π²ΠΈΠΊΠ»ΠΈΠΊΠΈ:
- Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π°Π½ΠΈΡ Ρ BigQuery Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ dbt Core.
- Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΉ ΠΎΠ½ΠΎΠ²Π»Π΅Π½Π½Ρ data marts Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ Π±ΡΠ·Π½Π΅Ρ-Π²ΠΈΠΌΠΎΠ³
- ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΠΊΠΎΡΡΡ, Π½Π°Π΄ΡΠΉΠ½ΠΎΡΡΡ ΡΠ° ΡΠ·Π³ΠΎΠ΄ΠΆΠ΅Π½ΠΎΡΡΡ Π΄Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π· Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΡΡΠ°ΡΠ½ΠΈΡ ΠΏΡΠ΄Ρ ΠΎΠ΄ΡΠ² Ρ ΠΏΡΠ°ΠΊΡΠΈΠΊ Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ Π΄Π°Π½ΠΈΡ ΡΠ° data engineering.
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· Π°Π½Π°Π»ΡΡΠΈΠΊΠ°ΠΌΠΈ ΡΠ° ΠΏΡΠΎΠ΄Π°ΠΊΡ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΡΠ°ΠΌΠΈ.
Π©ΠΎ Π΄Π»Ρ Π½Π°Ρ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ:
- ΠΡΠ΄ 3 ΡΠΎΠΊΡΠ² ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ Ρ ΡΡΠ΅ΡΡ Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ Π°Π±ΠΎ data engineering.
- ΠΡΠ΄ 2 ΡΠΎΠΊΡΠ² Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ±ΠΎΡΠΈ Π· dbt: ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ, ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΠΉ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ; Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΠΌΠ°ΠΊΡΠΎΡΡΠ², sources, seeds Ρ variables; ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΡΡΡΡΠΊΡΡΡΠΈ dbt-ΠΏΡΠΎΡΠΊΡΡ.
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· BigQuery Π°Π±ΠΎ ΡΠ½ΡΠΈΠΌΠΈ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΠΌΠΈ data warehouses: Snowflake, Redshift, Databricks.
- ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ Π·Π½Π°Π½Π½Ρ SQL ΡΠ° Π²ΠΌΡΠ½Π½Ρ ΠΏΠΈΡΠ°ΡΠΈ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½Ρ, ΡΠΈΡΠ°Π±Π΅Π»ΡΠ½Ρ Π·Π°ΠΏΠΈΡΠΈ.
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² OLAP-Π°ΡΡ ΡΡΠ΅ΠΊΡΡΡΠΈ ΡΠ° ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ Π·Π°ΠΏΠΈΡΡΠ² (partitioning, clustering, cost optimization).
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ² ΡΠΎΠ±ΠΎΡΠΈ Π· Π΄Π°Π½ΠΈΠΌΠΈ ΡΠ° Π·Π΄Π°ΡΠ½ΡΡΡΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΡΠ²Π°ΡΠΈ Π±ΡΠ·Π½Π΅Ρ-Π²ΠΈΠΌΠΎΠ³ΠΈ Ρ ΡΡΡΡΠΊΡΡΡΠΎΠ²Π°Π½Ρ Π΄Π°ΡΠ°-ΠΌΠΎΠ΄Π΅Π»Ρ.
ΠΡΠ΄Π΅ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ:
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² ETL/ELT ΡΠ° ΠΏΡΠ΄Ρ ΠΎΠ΄ΡΠ² Π΄ΠΎ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ Π΄Π°Π½ΠΈΡ .
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Git ΡΠ° ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡΠΌΠΈ CI/CD.
- ΠΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· BI-ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΠΌΠΈ: Tableau Π°Π±ΠΎ ΡΡ Π½Ρ Π°Π½Π°Π»ΠΎΠ³ΠΈ.
- ΠΠΎΡΠ²ΡΠ΄ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ² Π΄Π»Ρ ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ ΡΠΊΠΎΡΡΡ Π΄Π°Π½ΠΈΡ : Great Expectations, dbt tests ΡΠΎΡΠΎ.
ΠΡΠΎ Π½Π°Ρ
6037 β Π²Π΅Π½ΡΡΡΠ½Π΅ ΠΏΠ°ΡΡΠ½Π΅ΡΡΡΠ²ΠΎ, ΡΠΎ ΡΠ½Π²Π΅ΡΡΡΡ Π² ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ Π½Π° ΡΠ°Π½Π½ΡΡ Π΅ΡΠ°ΠΏΠ°Ρ . ΠΠΈ ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΡΡΠΌΠΎ ΡΡΠ°ΡΡΠ°ΠΏΠΈ Π½Π° Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½Ρ ΡΠ° ΡΡΡΠΉΠΊΡ Π±ΡΠ·Π½Π΅ΡΠΈ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ΠΎΠ²ΠΎΡ, Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΎΡ ΡΠ° ΠΎΠΏΠ΅ΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·ΠΈ.
ΠΠ°ΡΠ° ΠΌΡΡΡΡ β ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΠΈΡΠΈ Π£ΠΊΡΠ°ΡΠ½Ρ Π· Π°ΡΡΡΠΎΡΡ-Ρ Π°Π±Ρ Π½Π° ΠΊΡΠ°ΡΠ½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ². ΠΠΈ Π½Π°ΡΡΠ»Π΅Π½Ρ ΠΏΠΎΠ±ΡΠ΄ΡΠ²Π°ΡΠΈ ΠΏΠΎΡΡΡΠ΅Π»Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΈΡ Π»ΡΠ΄Π΅ΡΡΠ² Ρ ΡΠ²ΠΎΡΡ Π½ΡΡΠ°Ρ ΡΠ· ΡΡΠΊΡΠΏΠ½ΠΎΡ ΠΊΡΠ»ΡΠΊΡΡΡΡ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ² 1 ΠΌΡΠ»ΡΡΡΠ΄.
ΠΠ°ΡΡ ΡΡΠ½Π½ΠΎΡΡΡ
- Optimism. ΠΠ°ΡΡ Π»ΡΠ΄ΠΈ ΡΠΏΡΠΈΠΉΠΌΠ°ΡΡΡ ΠΊΠ°ΡβΡΡΡ ΡΠΊ ΠΌΠ°ΡΠ°ΡΠΎΠ½. ΠΠΎΠ½ΠΈ Π½Π΅ Π·Π±Π°Π²Π»ΡΡΡΡ ΡΠ΅ΠΌΠΏ Π½Π°Π²ΡΡΡ ΡΠΎΠ΄Ρ, ΠΊΠΎΠ»ΠΈ ΡΠΊΠ»Π°Π΄Π½ΠΎ, Π±ΠΎ ΡΠΎΠ·ΡΠΌΡΡΡΡ: Ρ ΡΡΠ½Π°Π»Ρ β Π΄ΠΎΡΡΠ³Π½Π΅Π½Π½Ρ Π²Π΅Π»ΠΈΠΊΠΎΡ ΡΡΠ»Ρ.
- Ownership. Π£ 6037 Π½Π΅ΠΌΠ°Ρ Π·Π°ΠΏΠ°ΡΠ½ΠΈΡ . ΠΠΎΠΆΠ΅Π½ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Ρ Π·Π° ΡΠ²ΠΎΡ Π΄ΡΠ»ΡΠ½ΠΊΡ ΠΌΠ°ΡΡΡΡΡΡ, Π±ΠΎ Π²ΡΠ΄ ΠΎΡΠΎΠ±ΠΈΡΡΠΎΠ³ΠΎ Π²Π½Π΅ΡΠΊΡ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Π·Π°Π»Π΅ΠΆΠΈΡΡ ΡΡΠΏΡΡ ΡΡΡΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ.
- Diligence. ΠΠΈ ΠΏΡΠ°ΡΡΡΠΌΠΎ Π·Π° ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠΌ ΠΊΠΎΠΌΠ°Π½Π΄ Π€ΠΎΡΠΌΡΠ»ΠΈ-1, Π΄Π΅ ΡΠ²ΠΈΠ΄ΠΊΡΡΡΡ ΠΏΠΎΡΠ΄Π½Π°Π½Π° Π· ΡΠΎΡΠ½ΡΡΡΡ. ΠΠΈ ΠΌΠΈΡΠ»ΠΈΠΌΠΎ Π»ΠΎΠ³ΡΡΠ½ΠΎ Ρ ΡΠΈΡΡΠ°Ρ Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½ΡΡ , Π±ΠΎ Π²ΡΡΠΈΠΌΠΎ, ΡΠΎ Π³Π»ΠΈΠ±ΠΈΠ½Π° Π°Π½Π°Π»ΡΠ·Ρ ΡΠΎΡΠΌΡΡ ΡΡΠ°Π±ΡΠ»ΡΠ½Ρ ΠΏΠ΅ΡΠ΅Π²Π°Π³Ρ.
Π©ΠΎ Π½Π°Ρ Π²ΡΠ΄ΡΡΠ·Π½ΡΡ:
ΠΠΈ Π½Π΅ ΠΎΠ±ΡΡΡΡΠΌΠΎ ΡΠ΅, ΡΠΎΠ³ΠΎ Π½Π΅ ΠΌΠΎΠΆΠ΅ΠΌΠΎ Π²ΠΈΠΊΠΎΠ½Π°ΡΠΈ. ΠΠ΅ Π³Π°ΡΠ°Π½ΡΡΡΠΌΠΎ Π»Π΅Π³ΠΊΠΎΠ³ΠΎ ΡΠ»ΡΡ Ρ ΡΠΈ ΠΏΠΎΠ²Π΅ΡΡ Π½Π΅Π²ΠΎΠ³ΠΎ ΠΊΠΎΠΌΡΠΎΡΡΡ. ΠΠ°ΡΠΎΠΌΡΡΡΡ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ ΡΡΠ°ΡΠΊΡΠΎΡΡΡ Π΄ΠΎ Π²ΡΠ΄ΡΡΡΠ½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ².
- ΠΠΌΠ°Π³Π°Π½Π½Ρ Ρ ΠΠΈΡΡΠΉ Π»ΡΠ·Ρ. Π―ΠΊΡΠΎ 6037 ΠΏΡΠ°ΡΡΡ Π· ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠΌ, ΡΠΎ ΡΡΠ»ΡΠΊΠΈ Π΄Π»Ρ ΡΠΎΠ³ΠΎ, Π°Π±ΠΈ Π·ΠΌΠ°Π³Π°ΡΠΈΡΡ Π· Π»ΡΠ΄Π΅ΡΠ°ΠΌΠΈ Π½Π° ΡΠ²ΡΡΠΎΠ²ΡΠΉ Π°ΡΠ΅Π½Ρ. ΠΡΠ°ΡΡΠ²Π°ΡΠΈ ΡΡΡ β ΡΠ΅ Π·ΠΌΠ°Π³Π°ΡΠΈΡΡ Π½Π° ΡΠ΅ΠΌΠΏΡΠΎΠ½Π°ΡΡ ΡΠ²ΡΡΡ. Π‘Π°ΠΌΠ΅ ΡΠΎΠΌΡ ΠΌΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ Π»ΡΠ΄Π΅ΠΉ Π· ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΡΡΡ Π°ΡΠ»Π΅ΡΠ°, Π³ΠΎΡΠΎΠ²ΠΈΡ Π²ΠΈΠΊΠ»Π°Π΄Π°ΡΠΈΡΡ ΡΠ° Π±ΠΎΡΠΎΡΠΈΡΡ Π·Π° ΠΏΠ΅ΡΡΠ΅ ΠΌΡΡΡΠ΅.
- Π‘ΠΏΡΠ»ΡΠ½ΠΎΡΠ° ΡΠΈΠ»ΡΠ½ΠΈΡ . ΠΠΎΠΌΠ°Π½Π΄Π½ΠΈΠΉ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Π·Π°Π»Π΅ΠΆΠΈΡΡ Π²ΡΠ΄ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Π³ΡΠ°Π²ΡΡ. Π’ΡΡ Π»ΡΠ΄ΠΈ Π·Π°Π΄Π°ΡΡΡ Π²ΠΈΡΠΎΠΊΡ ΠΏΠ»Π°Π½ΠΊΡ ΡΠΎΠ±Ρ ΠΉ ΡΠ½ΡΠΈΠΌ ΡΠ° ΡΠΏΠΎΠ½ΡΠΊΠ°ΡΡΡ ΠΎΠ΄Π½Π΅ ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈΡΡ. Π’ΠΎΠΌΡ ΡΠΈ Π°Π±ΠΎ Π·ΡΠΎΡΡΠ°ΡΡ Ρ Π·ΠΌΠ°Π³Π°ΡΡΡΡ Ρ ΠΠΈΡΡΠΉ Π»ΡΠ·Ρ, Π°Π±ΠΎ ΡΡ ΠΎΠ΄ΠΈΡ ΡΠ· Π΄ΠΈΡΡΠ°Π½ΡΡΡ.
- ΠΠ°ΡΡΠ½Π΅ΡΡΡΠ²ΠΎ, Π° Π½Π΅ Π½Π°ΠΉΠΌ. ΠΠΈ Π²ΡΠ΄ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΎΡΡΠΊΡΡΠΌΠΎ Π΄ΡΠΉ ΡΠΊ Π²ΡΠ΄ Π±ΡΠ·Π½Π΅Ρ-ΠΏΠ°ΡΡΠ½Π΅ΡΠ°, Π±ΠΎ Π² 6037 Π½Π°ΠΏΠΎΠ»Π΅Π³Π»ΠΈΠ²ΡΡΡΡ ΡΠ° ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π²Π½Π΅ΡΠΊΠ°ΠΌΠΈ ΠΌΠΎΠΆΠ½Π° Π·Π°ΡΠ»ΡΠΆΠΈΡΠΈ ΡΡ ΡΠΎΠ»Ρ. Π―ΠΊΡΠΎ Ρ ΠΎΡΠ΅Ρ ΡΡΠΎΠ³ΠΎ ΡΠ° Π½Π΅ Π·ΡΠΉΠ΄Π΅Ρ ΡΠ· Π΄ΠΈΡΡΠ°Π½ΡΡΡ β ΡΡΠ°Π½Π΅Ρ ΠΏΠ°ΡΡΠ½Π΅ΡΠΎΠΌ ΡΠ° ΠΎΡΡΠΈΠΌΠ°ΡΡ ΡΠ°ΡΡΠΊΡ Π² Π±ΡΠ·Π½Π΅ΡΡ.
- ΠΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ Π·Π°Π»Π΅ΠΆΠΈΡΡ Π²ΡΠ΄ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΈΠ½Π°Π³ΠΎΡΠΎΠ΄Π° Π·Π°Π»Π΅ΠΆΠΈΡΡ Π½Π΅ Π²ΡΠ΄ ΠΊΡΠ»ΡΠΊΠΎΡΡΡ Π³ΠΎΠ΄ΠΈΠ½, Π° Π²ΡΠ΄ ΡΠΊΠΎΡΡΡ ΡΠΎΠ±ΠΎΡΠΈ. ΠΠ°ΠΌΡΡΡΡ ΠΏΠΎΡΠΎΠΆΠ½ΡΡ ΠΎΠ±ΡΡΡΠ½ΠΎΠΊ ΠΌΠΈ Π΄Π°ΡΠΌΠΎ ΡΡΡΠΊΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ, ΡΠΊΡΠ»ΡΠΊΠΈ ΠΉ Π·Π° ΡΠΊΠΈΠΉ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Π·Π°ΡΠΎΠ±Π»ΡΡΠΈΠΌΠ΅Ρ Π½Π° ΠΊΠΎΠΆΠ½ΠΎΠΌΡ Π· ΡΡΠ²Π½ΡΠ².
Π©ΠΎ ΠΌΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- Health & Wellness
- Anniversary Benefits. Π©ΠΎΡΠΎΠΊΡ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΡΡΠΌΠΎ Π΄ΠΎ $2000 Π½Π° wellness Π°Π±ΠΎ ΡΠΏΠΎΡΡ, ΡΠΎΠ± Π·Π°Π»ΠΈΡΠ°ΡΠΈΡΡ Ρ ΡΠΎΡΠΌΡ Π½Π΅ Π»ΠΈΡΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΎ, Π° ΠΉ ΡΡΠ·ΠΈΡΠ½ΠΎ.
- Insurance & Health. ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ Π΄Π»Ρ ΡΠ΅Π±Π΅ ΡΠ° ΡΠΎΠ΄ΠΈΠ½ΠΈ, ΡΡΠΎΠΌΠ°ΡΠΎΠ»ΠΎΠ³ΡΡΠ½Π΅ ΠΏΠΎΠΊΡΠΈΡΡΡ ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³Π°.
- Talent & Growth
- Learning & Development. ΠΠΎΠΌΠΏΠ΅Π½ΡΡΡΠΌΠΎ Π±ΡΠ΄Ρ-ΡΠΊΡ Π½Π°Π²ΡΠ°Π»ΡΠ½Ρ ΡΠΎΡΠΌΠ°ΡΠΈ, ΡΠΎ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΡΡ Π·ΡΠΎΡΡΠ°ΡΠΈ Ρ 6037: Π²ΡΠ΄ ΠΊΡΡΡΡΠ² Ρ ΠΌΠ΅Π½ΡΠΎΡΡΡΠΊΠΈΡ ΠΏΡΠΎΠ³ΡΠ°ΠΌ Π΄ΠΎ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΡΠΉ Ρ ΡΠ΅ΡΡΠΈΡΡΠΊΠ°ΡΡΠΉ.
- Global Events Budget. ΠΠΈ Ρ ΠΎΡΠ΅ΠΌΠΎ, ΡΠΎΠ± ΡΠΈ ΡΠ΅ΡΠΏΠ°Π²(-Π»Π°) Π½Π°ΡΡ Π½Π΅Π½Π½Ρ Π²ΡΠ΄ ΠΊΡΠ°ΡΠΈΡ . Π©ΠΎΡΠΎΠΊΡ ΠΌΠΎΠΆΠ΅Ρ ΠΎΠ±ΡΠ°ΡΠΈ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ ΠΏΠΎΠ΄ΡΡ Ρ ΡΡΠ΅ΡΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ, ΠΊΡΠ΅Π°ΡΠΈΠ²Ρ ΡΠΈ Π±ΡΠ·Π½Π΅ΡΡ ΡΠ° Π²ΡΠ΄Π²ΡΠ΄Π°ΡΠΈ ΡΡ Π·Π° ΡΠ°Ρ ΡΠ½ΠΎΠΊ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
- Spot Award. Π©ΠΎΠΊΠ²Π°ΡΡΠ°Π»Ρ Π²ΡΠ΄Π·Π½Π°ΡΠ°ΡΠΌΠΎ ΠΊΠΎΠ»Π΅Π³ Π·Π° Π²ΠΈΠ½ΡΡΠΊΠΎΠ²Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ. Π¦Π΅ Π½Π°Ρ ΡΠΏΠΎΡΡΠ± ΠΏΠΎΠ΄ΡΠΊΡΠ²Π°ΡΠΈ ΡΠΈΠΌ, Ρ ΡΠΎ ΠΏΡΠΎΡΠ²ΠΈΠ² ΡΠ΅Π±Π΅ Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ ΠΏΡΠΎΡΠΊΡΠ°Ρ Π°Π±ΠΎ Π·ΡΠΎΠ±ΠΈΠ² ΠΏΠΎΠΌΡΡΠ½ΠΈΠΉ Π²Π½Π΅ΡΠΎΠΊ Ρ ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
- Work & Life
- All-inclusive ΠΎΡΡΡ. ΠΠΈ Π²ΡΡΠΈΠΌΠΎ Π² ΠΎΡΡΡ-first ΠΊΡΠ»ΡΡΡΡΡ, Π±ΠΎ Π½Π°ΠΉΠΊΡΠ°ΡΡ ΡΠ΄Π΅Ρ Π½Π°ΡΠΎΠ΄ΠΆΡΡΡΡΡΡ ΠΎΡΠ»Π°ΠΉΠ½. Π‘Π°ΠΌΠ΅ ΡΠΎΠΌΡ ΠΌΠΈ ΡΠΎΠ±ΠΈΠΌΠΎ Π²ΡΠ΅, ΡΠΎΠ± ΠΊΠΎΠΆΠ½ΠΎΠΌΡ Π±ΡΠ»ΠΎ ΠΊΠΎΠΌΡΠΎΡΡΠ½ΠΎ Π² ΠΎΡΡΡΠ°Ρ ΠΠΈΡΠ²Π° Π°Π±ΠΎ ΠΡΠ²ΠΎΠ²Π°. Π‘Π½ΡΠ΄Π°Π½ΠΊΠΈ, ΠΎΠ±ΡΠ΄ΠΈ, ΠΌΠ°ΡΠ°ΠΆΠ½ΠΈΠΉ ΠΊΠ°Π±ΡΠ½Π΅Ρ β ΡΠ΅ Π΄Π°Π»Π΅ΠΊΠΎ Π½Π΅ Π²ΡΠ΅, ΡΠΎ ΡΠ΅ΠΊΠ°Ρ ΡΠ΅Π±Π΅ Π² Π½Π°ΡΠΈΡ ΠΏΡΠΎΡΡΠΎΡΠ°Ρ .
- Travel. Π ΠΎΠ±ΠΎΡΡ Π²ΠΈΡΡΠ°ΡΠΈ Π½Π° ΠΏΡΠΎΡΠ·Π΄ ΡΠ° ΠΏΡΠΎΠΆΠΈΠ²Π°Π½Π½Ρ ΠΌΠΈ ΠΏΠΎΠ²Π½ΡΡΡΡ ΠΊΠΎΠΌΠΏΠ΅Π½ΡΡΡΠΌΠΎ. ΠΠΈ ΠΏΡΠ°ΡΡΡΠΌΠΎ Π·Π° ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠΌ ownership, ΡΠΎΠΌΡ Π΄ΠΎΠ²ΡΡΡΡΠΌΠΎ ΡΠ²ΠΎΡΠΌΡ ΡΡΡΠ΅Π½Π½Ρ ΡΠΎΠ΄ΠΎ Π²ΠΈΡΡΠ°Ρ, ΡΠΊΡ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΡΡ ΡΠΏΡΠ»ΡΠ½ΠΎΠΌΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ.
- Doubling Donations. 6037 ΠΏΠΎΠ΄Π²ΠΎΡΡ ΡΠ²ΠΎΡ Π΄ΠΎΠ½Π°ΡΠΈ Π½Π° Π²ΡΠΉΡΡΠΊΠΎΠ²Ρ Π΄ΠΎΠΏΠΎΠΌΠΎΠ³Ρ, Π°Π±ΠΈ ΠΏΠΎΡΠΈΠ»ΠΈΡΠΈ Π²ΠΏΠ»ΠΈΠ² ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Π²Π½Π΅ΡΠΊΡ.
ΠΠΈΡΠΈΡΠ½ΠΈ ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌ Π·Ρ ΡΠ²ΠΎΡΡ ΠΊΠ°ΡΚΌΡΡΠΈ β ΠΏΡΠΈΠΉΠΌΠ°ΠΉ Π²ΠΈΠΊΠ»ΠΈΠΊ ΡΠ° Π½Π°Π΄ΡΠΈΠ»Π°ΠΉ CV.
Π―ΠΊΡΠΎ ΡΠΈ Π½Π΅ ΡΡΠΊΠ°ΡΡ ΡΠΎΠ±ΠΎΡΡ, Π°Π»Π΅ ΠΌΠ°ΡΡ Π΄ΡΡΠ·ΡΠ² Π°Π±ΠΎ Π·Π½Π°ΠΉΠΎΠΌΠΈΡ , ΡΠΊΡ Π² ΠΏΠΎΡΡΠΊΡ β ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΡ Π²ΡΡΠ°ΡΡΡΡΡ. ΠΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΡ Π½Π° ΡΡ ΠΏΠΎΠ·ΠΈΡΡΡ ΡΠΈ ΠΎΡΡΠΈΠΌΠ°ΡΡ Π±ΠΎΠ½ΡΡ $600 + $600 ΠΌΠΈ Π·Π°Π΄ΠΎΠ½Π°ΡΠΈΠΌΠΎ Ρ ΡΠΎΠ½Π΄ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ ΠΠ‘Π£ Π·Π° ΡΠ²ΠΎΡΠΌ Π²ΠΈΠ±ΠΎΡΠΎΠΌ!
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Β· 28 views Β· 1 application Β· 3d
SQL Development Team Lead
Ukraine Β· Product Β· 3 years of experience Β· English - NoneΠΡΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ: ΠΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΈΠΊ - Π½Π°ΠΉΠ±ΡΠ»ΡΡΠ° ΠΌΠ΅ΡΠ΅ΠΆΠ° Π°ΠΏΡΠ΅ΠΊ Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΡΠΊΠ° ΡΠ²ΠΈΠ΄ΠΊΠΎ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΡΡΡΡ. ΠΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΡΡΠΌΠΎ Π½ΠΎΠ²Ρ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ ΡΠ° ΡΠ½Π½ΠΎΠ²Π°ΡΡΡ Ρ ΡΡΠ΅ΡΡ ΡΠ°ΡΠΌΠ°ΡΡΡ. ΠΠ°ΡΡΠΉ ΠΊΠΎΠΌΠ°Π½Π΄Ρ Π½Π΅ΠΎΠ±Ρ ΡΠ΄Π½ΠΈΠΉ ΡΠ°Ρ ΡΠ²Π΅ΡΡ, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π½Π°ΠΌ ΡΡΡΡΠΊΡΡΡΡΠ²Π°ΡΠΈ ΡΠ° Π°Π½Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ Π²Π΅Π»ΠΈΠΊΡ ΠΎΠ±ΡΡΠ³ΠΈ...ΠΡΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ: ΠΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΈΠΊ - Π½Π°ΠΉΠ±ΡΠ»ΡΡΠ° ΠΌΠ΅ΡΠ΅ΠΆΠ° Π°ΠΏΡΠ΅ΠΊ Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΡΠΊΠ° ΡΠ²ΠΈΠ΄ΠΊΠΎ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΡΡΡΡ. ΠΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΡΡΠΌΠΎ Π½ΠΎΠ²Ρ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ ΡΠ° ΡΠ½Π½ΠΎΠ²Π°ΡΡΡ Ρ ΡΡΠ΅ΡΡ ΡΠ°ΡΠΌΠ°ΡΡΡ. ΠΠ°ΡΡΠΉ ΠΊΠΎΠΌΠ°Π½Π΄Ρ Π½Π΅ΠΎΠ±Ρ ΡΠ΄Π½ΠΈΠΉ ΡΠ°Ρ ΡΠ²Π΅ΡΡ, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π½Π°ΠΌ ΡΡΡΡΠΊΡΡΡΡΠ²Π°ΡΠΈ ΡΠ° Π°Π½Π°Π»ΡΠ·ΡΠ²Π°ΡΠΈ Π²Π΅Π»ΠΈΠΊΡ ΠΎΠ±ΡΡΠ³ΠΈ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ Π±ΡΠ·Π½Π΅Ρ-ΡΡΡΠ΅Π½Ρ.
ΠΠ°Π²Π΄Π°Π½Π½Ρ Π½Π° ΠΏΠΎΡΠ°Π΄Ρ:
β’ ΠΡΠΎΡΠΊΡΡΠ²Π°Π½Π½Ρ, ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΡΡ ΠΎΠ²ΠΈΡ Π΄Π°Π½ΠΈΡ (DWH), ΡΠΎ ΠΏΡΠ΄ΡΡΠΈΠΌΡΡΡΡ Π±ΡΠ·Π½Π΅Ρ-Π°Π½Π°Π»ΡΡΠΈΠΊΡ ΡΠ° Π·Π²ΡΡΡΠ²Π°Π½Π½Ρ.
β’ Π‘ΡΠ²ΠΎΡΠ΅Π½Π½Ρ, Π½Π°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° OLAP ΠΊΡΠ±ΡΠ² Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ ΡΠ° Π±ΡΠ·Π½Π΅Ρ-Π·Π²ΡΡΠ½ΠΎΡΡΡ.
β’ ΠΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΡΡΠ΅Π½Ρ Π΄Π»Ρ ΠΏΠΎΠΊΡΠ°ΡΠ΅Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΈ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Ρ Π·Π²ΡΡΡΠ² ΡΠ° Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ OLAP ΠΊΡΠ±ΡΠ².
β’ ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΠΊΠΎΡΠ΅ΠΊΡΠ½ΠΎΠ³ΠΎ Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ Π΄Π°Π½ΠΈΡ Π· ΡΡΠ·Π½ΠΈΡ Π΄ΠΆΠ΅ΡΠ΅Π» Π΄ΠΎ OLAP ΠΊΡΠ±ΡΠ² Π΄Π»Ρ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ Π·Π°ΠΏΠΈΡΡΠ².
β’ ΠΠ½Π°Π»ΡΠ· ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠ° ΠΊΠΎΡΠ΅ΠΊΡΠ½ΠΎΡΡΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ Ρ ΠΊΡΠ±Π°Ρ , ΠΏΠΎΡΡΠΊ ΡΠ° Π²ΠΈΠΏΡΠ°Π²Π»Π΅Π½Π½Ρ ΠΏΠΎΠΌΠΈΠ»ΠΎΠΊ Π°Π±ΠΎ Π½Π΅Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈΡ Π·Π°ΠΏΠΈΡΡΠ². ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ DWH ΡΠ° OLAP ΡΡΡΠ΅Π½Ρ ΡΠ΅ΡΠ΅Π· Π½Π°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ½Π΄Π΅ΠΊΡΡΠ², ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ Π·Π°ΠΏΠΈΡΡΠ² Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ² Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ Π΄Π°Π½ΠΈΡ .
β’ ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ² Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ ΡΠ° OLAP ΠΊΡΠ±ΡΠ², Π΄ΠΎΠΏΠΎΠΌΠΎΠ³Π° Ρ ΡΠΎΡΠΌΡΠ»ΡΠ²Π°Π½Π½Ρ Π·Π°ΠΏΠΈΡΡΠ² Π΄Π»Ρ ΠΎΡΡΠΈΠΌΠ°Π½Π½Ρ Π½Π΅ΠΎΠ±Ρ ΡΠ΄Π½ΠΎΡ Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ.
β’ ΠΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³ ΡΡΠ°Π½Ρ DWH ΡΠ° OLAP ΠΊΡΠ±ΡΠ², Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΡΠ΅Ρ Π½ΡΡΠ½ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌ Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΡ Π½ΡΠΎΡ ΡΡΠ°Π±ΡΠ»ΡΠ½ΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ.
ΠΠΈΠΌΠΎΠ³ΠΈ Π΄ΠΎ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ°:
β’ Π Π΅Π»ΡΡΡΠΉΠ½Ρ Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ : ΠΠ»ΠΈΠ±ΠΎΠΊΡ Π·Π½Π°Π½Π½Ρ SQL Ρ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΠ΅Π»ΡΡΡΠΉΠ½ΠΈΠΌΠΈ Π±Π°Π·Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ (MS SQL Server, Oracle, MySQL).
β’ DWH ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ: Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π°ΡΡ ΡΡΠ΅ΠΊΡΡΡΠΈ ΡΡ ΠΎΠ²ΠΈΡ Π΄Π°Π½ΠΈΡ Ρ ΠΊΡΠ°ΡΠΈΡ ΠΏΡΠ°ΠΊΡΠΈΠΊ ΡΡ Π½ΡΠΎΠ³ΠΎ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ.
β’ OLAP ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ: ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· OLAP ΠΊΡΠ±Π°ΠΌΠΈ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈ ΡΠΎΠ·ΡΠΎΠ±ΠΊΡ, Π½Π°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ Ρ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ.
β’ ETL ΠΏΡΠΎΡΠ΅ΡΠΈ: ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΠΌΠΈ Π΄Π»Ρ Π²ΠΈΠ»ΡΡΠ΅Π½Π½Ρ, ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ ΡΠ° Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ (ETL) Π΄Π°Π½ΠΈΡ Ρ DWH.
β’ ΠΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ Π·Π°ΠΏΠΈΡΡΠ²: ΠΠ½Π°Π½Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ SQL Π·Π°ΠΏΠΈΡΡΠ² Ρ Π½Π°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ½Π΄Π΅ΠΊΡΡΠ² Π΄Π»Ρ ΠΏΡΠ΄Π²ΠΈΡΠ΅Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π±Π°Π· Π΄Π°Π½ΠΈΡ .
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- ΠΡΠΎΠ½ΡΠ²Π°Π½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ²;
- ΠΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½Ρ ΠΏΠ»Π°ΡΡ;
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΎΠ³ΠΎ ΡΠΎΠ·Π²ΠΈΡΠΊΡ ΡΠ° Π½Π°Π²ΡΠ°Π½Π½Ρ;
- ΠΠ½ΡΡΠΊΠΈΠΉ Π³ΡΠ°ΡΡΠΊ ΡΠΎΠ±ΠΎΡΠΈ ΡΠ° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΎ;
Π ΠΎΠ±ΠΎΡΡ Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ², Π½Π°ΡΡΠ»Π΅Π½ΠΈΡ Π½Π° Π΄ΠΎΡΡΠ³Π½Π΅Π½Π½Ρ ΡΠΏΡΠ»ΡΠ½ΠΈΡ ΡΡΠ»Π΅ΠΉ.
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Β· 79 views Β· 2 applications Β· 4d
Data Engineer (Analytics Engineer)
Office Work Β· Ukraine (Kyiv) Β· Product Β· 2 years of experience Β· English - NoneViyarTech - ΠΌΡΡΡΠ΅, Π΄Π΅ IT-ΡΠ½Π½ΠΎΠ²Π°ΡΡΡ Π·ΡΡΡΡΡΡΠ°ΡΡΡΡΡ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²ΠΎΠΌ. ΠΠΈ - ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²Π° IT-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π² Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΡ Viyar, Π»ΡΠ΄Π΅ΡΠ° ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ½ΠΊΡ Π· Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π° ΠΌΠ΅Π±Π»Π΅Π²ΠΈΡ Π΄Π΅ΡΠ°Π»Π΅ΠΉ. ΠΠΎΠΆΠ½Π° Π»ΡΠ½ΡΠΉΠΊΠ° Π½Π°ΡΠΎΠ³ΠΎ ΠΊΠΎΠ΄Ρ Π±Π΅Π·ΠΏΠΎΡΠ΅ΡΠ΅Π΄Π½ΡΠΎ Π²ΠΏΠ»ΠΈΠ²Π°Ρ Π½Π° Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡ Π»ΡΠ½ΡΡ. 180+...ViyarTech - ΠΌΡΡΡΠ΅, Π΄Π΅ IT-ΡΠ½Π½ΠΎΠ²Π°ΡΡΡ Π·ΡΡΡΡΡΡΠ°ΡΡΡΡΡ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²ΠΎΠΌ.
ΠΠΈ - ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²Π° IT-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π² Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΡ Viyar, Π»ΡΠ΄Π΅ΡΠ° ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ½ΠΊΡ Π· Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π° ΠΌΠ΅Π±Π»Π΅Π²ΠΈΡ Π΄Π΅ΡΠ°Π»Π΅ΠΉ. ΠΠΎΠΆΠ½Π° Π»ΡΠ½ΡΠΉΠΊΠ° Π½Π°ΡΠΎΠ³ΠΎ ΠΊΠΎΠ΄Ρ Π±Π΅Π·ΠΏΠΎΡΠ΅ΡΠ΅Π΄Π½ΡΠΎ Π²ΠΏΠ»ΠΈΠ²Π°Ρ Π½Π° Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡ Π»ΡΠ½ΡΡ.
180+ ΡΠΏΠ΅ΡΡΠ°Π»ΡΡΡΡΠ² ViyarTech ΡΡΠ²ΠΎΡΡΡΡΡ IT-ΡΡΡΠ΅Π½Π½Ρ Π΄Π»Ρ ΠΏΠΎΠ²Π½ΠΎΠ³ΠΎ ΡΠΈΠΊΠ»Ρ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π° ΠΌΠ΅Π±Π»ΡΠ² - Π²ΡΠ΄ ΡΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ ΡΡΡΠ°ΡΠ½ΠΈΠΌΠΈ ΡΠ΅Ρ Π°ΠΌΠΈ Π΄ΠΎ Π²Π΅Π± ΡΠ΅ΡΠ²ΡΡΡΠ² Π΄Π»Ρ ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΡΠΊΡΠ°ΡΠ½ΡΡΠ².
ΠΠΈ ΡΡΠΊΠ°ΡΠΌΠΎ Π΄ΠΎΡΠ²ΡΠ΄ΡΠ΅Π½ΠΎΠ³ΠΎ Data Engineer (Analytics Engineer), Π² ΠΊΡΠΎΡΡΡΡΠ½ΠΊΡΡΠΎΠ½Π°Π»ΡΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄Ρ Growth.
ΠΠ°ΠΆΠ»ΠΈΠ²ΠΎ! ΠΠΈ ΡΠ°ΡΡΠΈΠ½Π° Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΠΈ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΠΎΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Ρ ΠΏΡΠ°ΡΡΡΠΌΠΎ Π² ΠΎΡΡΡΡ.
π ΠΠ°ΡΠ° Π°Π΄ΡΠ΅ΡΠ°: ΠΡΠ»ΡΠ²Π°Ρ ΠΠ°ΡΠ»Π°Π²Π° ΠΠ°Π²Π΅Π»Π° 6 (ΠΠ΅ΡΡΠΎ ΠΠ΅ΡΠ΅ΡΡΠ΅ΠΉΡΡΠΊΠ°)
Π’Π²ΠΎΡ Π·Π°Π΄Π°ΡΡ:- ΠΡΡ ΡΡΠ΅ΠΊΡΡΡΠ° Π΄Π°Π½ΠΈΡ : ΠΡΠΎΡΠΊΡΡΠ²Π°Π½Π½Ρ Π»ΠΎΠ³ΡΡΠ½ΠΎΡ ΡΡΡΡΠΊΡΡΡΠΈ ΡΡ ΠΎΠ²ΠΈΡΠ° ΡΠ° Π½Π°Π»Π°Π³ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π²'ΡΠ·ΠΊΡΠ² ΠΌΡΠΆ ΡΠΎΠ·ΡΡΠ·Π½Π΅Π½ΠΈΠΌΠΈ Π΄ΠΆΠ΅ΡΠ΅Π»Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ .
- Data Pipelines: ΠΠ°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ Π·Π±ΠΎΡΡ ΡΠ° ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ (ETL/ELT) ΡΠ· Π·ΠΎΠ²Π½ΡΡΠ½ΡΡ ΡΠ΅ΡΠ²ΡΡΡΠ² ΡΠ° Π²Π½ΡΡΡΡΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌ.
- Data Quality: ΠΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΠΈΡΡΠ΅ΠΌΠΈ Π°Π²ΡΠΎΡΠ΅ΡΡΡΠ² ΡΠ° Π°Π»Π΅ΡΡΠΈΠ½Π³Ρ Π½Π° Π°Π½ΠΎΠΌΠ°Π»ΡΠ½Ρ Π·Π½Π°ΡΠ΅Π½Π½Ρ Π΄Π»Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π²ΠΈΡΠΎΠΊΠΎΡ ΡΠΊΠΎΡΡΡ Π΄Π°Π½ΠΈΡ .
- Data Marts: ΠΡΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π²ΡΡΡΠΈΠ½ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ ΠΎΠ±ΡΠΈΡΠ»Π΅Π½Π½Ρ Π±ΡΠ·Π½Π΅Ρ-ΠΌΠ΅ΡΡΠΈΠΊ ΡΠ° ΠΏΠΎΠ΄Π°Π»ΡΡΠΎΡ Π²ΡΠ·ΡΠ°Π»ΡΠ·Π°ΡΡΡ.
- Data Cleansing: ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΡΠ»ΡΡΠ½ΠΎΡΡΡ ΡΠ° ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΡΡ Π΄Π°Π½ΠΈΡ Π½Π° Π²ΡΡΡ Π΅ΡΠ°ΠΏΠ°Ρ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ.
- A/B Testing: ΠΠ°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ A/B ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠΈ Π΄Π»Ρ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ².
ΠΠ΅ΠΎΠ±Ρ ΡΠ΄Π½Π° Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·Π°:
Π¨ΡΠΊΠ°ΡΠΌΠΎ Data Engineer (Analytics Engineer) Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ Growth, Π½Π°ΠΏΡΡΠΌ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²Π° ΡΠ° ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ Π°Π½Π°Π»ΡΡΠΈΠΊΠ°.ΠΡΠ΄ΠΈΠ½Π° Π½Π° ΡΡΠΉ ΠΏΠΎΠ·ΠΈΡΡΡ ΠΌΠ°ΡΠΈΠΌΠ΅ Π·Π½Π°ΡΠ½ΠΈΠΉ Π²ΠΏΠ»ΠΈΠ² Π½Π° ΡΠΎΠ·ΠΈΡΠΎΠΊ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, Π½Π° Π·Π°Π»ΡΡΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ² ΡΠ° Π±Π°ΡΠΈΡΠΈΠΌΠ΅ Π±ΡΠ·Π½Π΅Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π²ΡΠ΄ ΡΠ²ΠΎΡΡ ΡΠΎΠ±ΠΎΡΠΈ.
- ΠΠΎΡΠ²ΡΠ΄: 3+ ΡΠΎΠΊΠΈ Data Engineer Π°Π±ΠΎ Analytics Engineer.
- SQL: (MSSQL)
- Python: ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² Π΄Π»Ρ Π·Π±ΠΎΡΡ Π΄Π°Π½ΠΈΡ Π· ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ΠΎΠ²ΠΈΡ ΡΠ° Π²Π½ΡΡΡΡΡΠ½ΡΡ API.
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Airflow.
- dbt: Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΡΠ° ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ Π΄Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π· dbt.
- CΡΠ΅ΠΊ: MSSQL, Python, Airflow, dbt, BigQuery (GA4)
ΠΡΠΎΡΠ΅Ρ Π½Π°ΠΉΠΌΡ:
- ΠΠ½ΡΠ΅ΡΠ²ΚΌΡ Π· Π½Π°ΡΠΈΠΌ ΡΠ΅ΠΊΡΡΡΠ΅ΡΠΎΠΌ (ΠΎΠ½Π»Π°ΠΉΠ½, ΠΎΡΡΡΠ½ΡΠΎΠ²Π½ΠΎ 30 Ρ Π²)
- Π’Π΅Ρ Π½ΡΡΠ½Π΅ ΡΠ½ΡΠ΅ΡΠ²ΚΌΡ (ΠΎΡΠ»Π°ΠΉΠ½, ΠΎΡΡΡΠ½ΡΠΎΠ²Π½ΠΎ 1 Π³ΠΎΠ΄ Ρ Π²)
- ΠΡΠΈΠΉΠΌΠ°ΡΠΌΠΎ ΡΡΡΠ΅Π½Π½Ρ
ΠΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- Π¦ΡΠΊΠ°Π²Ρ Π·Π°Π²Π΄Π°Π½Π½Ρ ΡΠ° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΠΏΠ»ΠΈΠ²Π°ΡΠΈ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡ Π· Π²ΡΠ΅ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΌ ΡΠΌ'ΡΠΌ.
- ΠΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ Π΄Π»Ρ ΠΊΠ°ΡΚΌΡΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΡ (ΠΌΠ°ΡΡΠΈΡΡ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΡΠΉ).
- Π‘ΡΡΠ°ΡΠ½Π° ΡΠ° ΠΏΠΎΡΡΠΆΠ½Π° ΡΠΎΠ±ΠΎΡΠ° ΡΠ΅Ρ Π½ΡΠΊΠ°.
- Π‘ΠΎΡΡΠ°Π»ΡΠ½ΠΈΠΉ ΠΏΠ°ΠΊΠ΅Ρ (24 ΠΊ.Π΄ Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ, Π»ΡΠΊΠ°ΡΠ½ΡΠ½Ρ (50% Π²ΡΠ΄ ΡΡΠ°Π²ΠΊΠΈ)
- ΠΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ ΠΏΡΠΎΡΡΠ»ΡΠ½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ (50%), Π²Π½ΡΡΡΡΡΠ½Ρ ΡΡΠ΅Π½ΡΠ½Π³ΠΈ.
- ΠΡΠΎΠ³ΡΠ°ΠΌΠ° Mental Health (ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΈΠΉ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³, Π½ΡΡΡΠΈΡΡΠΎΠ»ΠΎΠ³).
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ Π·Π½ΠΈΠΆΠΊΠΈ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡΡΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ ΡΠ° ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ² (ΠΌΠ΅Π±Π»Ρ, ΡΠ΅Ρ Π½ΡΠΊΠ°, ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΡΡΡΡ).
- ΠΠ½ΠΈΠΆΠΊΠΈ Π½Π° Ρ Π°ΡΡΡΠ²Π°Π½Π½Ρ Π² ΡΠ΅ΡΡΠΎΡΠ°Π½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
- ΠΡΠ΄ΠΆΠ΅Ρ Π½Π° ΡΠΎΡΡΠ°Π»ΡΠ½Ρ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΡ/Π΄ΠΎΠΏΠΎΠΌΠΎΠ³Ρ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΠ°ΠΌ.
Π₯ΠΎΡΠ΅Ρ Π΄ΡΠ·Π½Π°ΡΠΈΡΡ ΠΏΡΠΎ Π½Π°Ρ Π±ΡΠ»ΡΡΠ΅?
Π‘ΡΠΎΡΡΠ½ΠΊΠ° ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π½Π° DOU: https://jobs.dou.ua/companies/viyartech/vacancies/
ΠΠ°ΡΡ Π½Π°ΠΏΡΡΠΌΠΊΠΈ:
π¨ ΠΠ»Π°ΡΠ½Ρ 2D/3D CAD ΡΠΈΡΡΠ΅ΠΌΠΈ
Core ΠΏΡΠΎΠ΄ΡΠΊΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. ΠΠΎΠ²Π½ΠΈΠΉ ΡΠΈΠΊΠ» β Π²ΡΠ΄ ΠΏΡΠΎΡΠΊΡΡΠ²Π°Π½Π½Ρ ΠΌΠ΅Π±Π»ΡΠ² Π΄ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ½ΠΎΡ Π³Π΅Π½Π΅ΡΠ°ΡΡΡ ΠΊΡΠ΅ΡΠ»Π΅Π½Ρ ΡΠ° ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°.
βοΈ ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΡΡ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°
Π£ΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ Π²Π΅ΡΡΡΠ°ΡΠ°ΠΌΠΈ Π² ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌΡ ΡΠ°ΡΡ, Π»ΠΎΠ³ΡΡΡΠΈΠΊΠ°, ΠΏΠ»Π°Π½ΡΠ²Π°Π½Π½Ρ, ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΡΠΊΠΎΡΡΡ. ΠΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ Π· ΠΎΠ±Π»Π°Π΄Π½Π°Π½Π½ΡΠΌ ΡΠ° IoT ΠΏΡΠΈΡΡΡΠΎΡΠΌΠΈ.
πΌ ERP ΡΠΈΡΡΠ΅ΠΌΠΈ
Enterprise-ΡΡΡΠ΅Π½Π½Ρ, ΡΠΊΠ΅ ΡΠ½ΡΠ΅Π³ΡΡΡ Π²ΡΡ ΠΏΡΠΎΡΠ΅ΡΠΈ: Π²ΡΠ΄ Π·Π°ΠΊΡΠΏΡΠ²Π΅Π»Ρ Π΄ΠΎ ΡΡΠ½Π°Π½ΡΡΠ². ΠΠ±ΡΠΎΠ±Π»ΡΡΠΌΠΎ ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ ΡΡΠ°Π½Π·Π°ΠΊΡΡΠΉ ΡΠΎΠΌΡΡΡΡΡ.
π ΠΠ΅Π±-ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ
ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΡΠ°ΠΉΡΠΈ ΡΠ° Π²Π½ΡΡΡΡΡΠ½Ρ ΡΠ΅ΡΠ²ΡΡΠΈ.
π¬ R&D
ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΡΠΌΠΎ Π· ML, ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΠΌΠΈ AI, ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² Π΄Π»Ρ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°.
ΠΠ°Π»ΠΈΡΠ°ΠΉ Π·Π°ΡΠ²ΠΊΡ ΡΠ° ΠΏΡΠΈΡΠ΄Π½ΡΠΉΡΡ Π΄ΠΎ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎ ΡΡΠ²ΠΎΡΡΡ ΠΌΠ°ΠΉΠ±ΡΡΠ½Ρ!
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Β· 48 views Β· 3 applications Β· 18d
Data Engineer
Office Work Β· Ukraine (Kyiv) Β· 2 years of experience Β· English - B1 MilTech πͺΠ ΠΎΠ±ΠΎΡΠ° Π² ΠΎΡΡΡΡ, ΠΠΈΡΠ², Π±ΡΠΎΠ½ΡΠ²Π°Π½Π½Ρ Eleven β ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³ΠΎΠ²Π° Π°Π³Π΅Π½ΡΡΡ, ΡΠΊΠ° ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΡΡΡΡΡΡ Π½Π° ΠΏΠΎΡΡΠΊΡ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ² Π΄Π»Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΠΉ Ρ Π³Π°Π»ΡΠ·Ρ Π²ΡΠΉΡΡΠΊΠΎΠ²ΠΈΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ. ΠΠ°ΡΠ° ΠΌΠ΅ΡΠ° β Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΠΈ ΠΎΠ±βΡΠ΄Π½ΡΠ²Π°ΡΠΈ ΡΠΈΡ , Ρ ΡΠΎ Π½Π°Π±Π»ΠΈΠΆΠ°Ρ Π΄ΠΎ ΠΏΠ΅ΡΠ΅ΠΌΠΎΠ³ΠΈ. Π ΠΎΠ±ΠΎΡΠΎΠ΄Π°Π²Π΅ΡΡ β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ,...Π ΠΎΠ±ΠΎΡΠ° Π² ΠΎΡΡΡΡ, ΠΠΈΡΠ², Π±ΡΠΎΠ½ΡΠ²Π°Π½Π½Ρ
Eleven β ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³ΠΎΠ²Π° Π°Π³Π΅Π½ΡΡΡ, ΡΠΊΠ° ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΡΡΡΡΡΡ Π½Π° ΠΏΠΎΡΡΠΊΡ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ² Π΄Π»Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΠΉ Ρ Π³Π°Π»ΡΠ·Ρ Π²ΡΠΉΡΡΠΊΠΎΠ²ΠΈΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ. ΠΠ°ΡΠ° ΠΌΠ΅ΡΠ° β Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΠΈ ΠΎΠ±βΡΠ΄Π½ΡΠ²Π°ΡΠΈ ΡΠΈΡ , Ρ ΡΠΎ Π½Π°Π±Π»ΠΈΠΆΠ°Ρ Π΄ΠΎ ΠΏΠ΅ΡΠ΅ΠΌΠΎΠ³ΠΈ.
Π ΠΎΠ±ΠΎΡΠΎΠ΄Π°Π²Π΅ΡΡ β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΊΠ° Π·Π°ΠΉΠΌΠ°ΡΡΡΡΡ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²ΠΎΠΌ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡΠ² Π΅Π»Π΅ΠΊΡΡΠΎΠ½ΡΠΊΠΈ ΡΠ° ΠΏΡΠΈΠ»Π°Π΄ΡΠ² Ρ ΡΡΠ΅ΡΡ ΠΎΠ±ΠΎΡΠΎΠ½Π½ΠΈΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ Π²ΡΠ΄ ΡΠ΄Π΅Ρ Π΄ΠΎ Π²ΡΡΠ»Π΅Π½Π½Ρ! ΠΠΎΠΌΠΏΠ°Π½ΡΡ Π³Π°ΡΠ°Π½ΡΡΡ ΠΏΠΎΡΠ΄Π½Π°Π½Π½Ρ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΎΡ ΠΌΠΎΡΠΈΠ²Π°ΡΡΡ Π· ΡΡΠ²ΡΠ΄ΠΎΠΌΠ»Π΅Π½Π½ΡΠΌ, ΡΠΎ ΡΠ²ΠΎΡ ΡΠΎΠ±ΠΎΡΠ° ΠΌΠ°Ρ ΠΏΡΡΠΌΠΈΠΉ, Π²ΠΈΠΌΡΡΡΠ²Π°Π»ΡΠ½ΠΈΠΉ ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ.
Π£ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ Ρ ΠΏΡΠΎΡΡΡΡ Π΄Π»Ρ ΡΠΌΡΠ»ΠΈΠ²ΠΈΡ ΡΡΡΠ΅Π½Ρ Π±Π΅Π· Π·Π°ΠΉΠ²ΠΎΡ Π±ΡΡΠΎΠΊΡΠ°ΡΡΡ β ΡΠΊΡΠΎ ΡΠ΄Π΅Ρ ΠΏΡΠ°ΡΡΡ, Π²ΠΎΠ½Π° Π·Π°ΠΏΡΡΠΊΠ°ΡΡΡΡΡ.ΠΡΠ²ΡΡΠ° ΡΠ° Π΄ΠΎΡΠ²ΡΠ΄
- ΠΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° Ρ ΡΡΠ΅ΡΡ ΠΊΠΎΠΌΠΏβΡΡΠ΅ΡΠ½ΠΈΡ Π½Π°ΡΠΊ, ΠΏΡΠΈΠΊΠ»Π°Π΄Π½ΠΎΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΠ½ΠΆΠ΅Π½Π΅ΡΡΡ, ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΠΉΠ½ΠΈΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ Π°Π±ΠΎ ΡΡΠΌΡΠΆΠ½ΠΈΡ ΡΠΏΠ΅ΡΡΠ°Π»ΡΠ½ΠΎΡΡΡΡ .
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π½Π° ΠΏΠΎΡΠ°Π΄Ρ Data Engineer / ETL Engineer / Big Data Engineer Π²ΡΠ΄ 2 ΡΠΎΠΊΡΠ².
- ΠΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ΠΎΠ±ΡΠΎΠ±ΠΊΠΎΡ ΡΠ° Π·Π±Π΅ΡΡΠ³Π°Π½Π½ΡΠΌ Π²Π΅Π»ΠΈΠΊΠΈΡ ΠΎΠ±ΡΡΠ³ΡΠ² Π΄Π°Π½ΠΈΡ .
Π’Π΅Ρ Π½ΡΡΠ½Ρ Π·Π½Π°Π½Π½Ρ ΡΠ° Π½Π°Π²ΠΈΡΠΊΠΈ
- ΠΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ ΠΌΠΎΠ²Π°ΠΌΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡΠ²Π°Π½Π½Ρ Python, SQL.
- ΠΠΎΡΠ²ΡΠ΄ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ETL/ELT-ΠΏΡΠΎΡΠ΅ΡΡΠ² Π΄Π»Ρ Π·Π±ΠΎΡΡ, ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ° Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½Ρ Π΄Π°Π½ΠΈΡ .
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π°ΡΡ ΡΡΠ΅ΠΊΡΡΡΠΈ Π±Π°Π· Π΄Π°Π½ΠΈΡ : ΡΠ΅Π»ΡΡΡΠΉΠ½Ρ (PostgreSQL, MySQL), Π½Π΅ΡΠ΅Π»ΡΡΡΠΉΠ½Ρ (MongoDB, Cassandra).
- ΠΠ½Π°Π½Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ Data Lake, Data Warehouse.
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Ρ ΠΌΠ°ΡΠ½ΠΈΠΌΠΈ ΡΠ΅ΡΠ²ΡΡΠ°ΠΌΠΈ (AWS, GCP, Azure) Π΄Π»Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ° Π·Π±Π΅ΡΡΠ³Π°Π½Π½Ρ Π΄Π°Π½ΠΈΡ .
- ΠΠ°Π²ΠΈΡΠΊΠΈ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π·Π°ΠΏΠΈΡΡΠ² Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ Π½Π°Π΄ΡΠΉΠ½ΠΈΡ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² Π΄Π°Π½ΠΈΡ .
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ Π΄Π°ΡΠ°ΡΠ΅ΡΡΠ² Π΄Π»Ρ Computer Vision (COCO-ΡΠΎΡΠΌΠ°Ρ)
- ΠΠΎΡΠ²ΡΠ΄ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ½Π³Ρ ΡΠΎΡΠΎ- ΡΠ° Π²ΡΠ΄Π΅ΠΎ-Π΄Π°Π½ΠΈΡ
ΠΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ Π· ML/AI-ΠΏΡΠΎΡΠΊΡΠ°ΠΌΠΈ (ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠ° Π΄Π°Π½ΠΈΡ Ρ ΠΌΠΎΠ΄Π΅Π»Ρ, ΡΠΎΠ±ΠΎΡΠ° Π· feature store).
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² DevOps / MLOps (Docker, Kubernetes, CI/CD).
- ΠΠ½Π°Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ² ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΡΡ Π΄Π°Π½ΠΈΡ (Dagster, MLFlow, KubeFlow, ClearML).
- ΠΡΠ°ΠΊΡΠΈΠΊΠ° Ρ ΡΡΠ΅ΡΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ΅Π½ΡΠΎΡΠ½ΠΈΡ / ΡΠ΅Π»Π΅ΠΌΠ΅ΡΡΠΈΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ (Π½Π°ΠΏΡΠΈΠΊΠ»Π°Π΄, IoT, UAV).
- ΠΠΎΡΠ²ΡΠ΄ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΡΠ· ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ Π°Π½ΠΎΡΠ°ΡΡΡ, ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ² Π΄Π»Ρ ΡΠΎΠ·ΠΌΡΡΠΊΠΈ.
ΠΡΠΎΠ±ΠΈΡΡΡ ΡΠΊΠΎΡΡΡ
- Π‘ΠΈΡΡΠ΅ΠΌΠ½Π΅ ΡΠ° Π°Π½Π°Π»ΡΡΠΈΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ.
- Π£Π²Π°ΠΆΠ½ΡΡΡΡ Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ ΡΠ° ΠΎΡΡΡΠ½ΡΠ°ΡΡΡ Π½Π° ΡΠΊΡΡΡΡ Π΄Π°Π½ΠΈΡ .
- ΠΠΌΡΠ½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ Π· Data Scientists, Software Engineers, R&D.
- ΠΡΠΎΠ°ΠΊΡΠΈΠ²Π½ΡΡΡΡ Ρ ΠΏΠΎΡΡΠΊΡ ΡΡΡΠ΅Π½Ρ Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ².
ΠΠ΅ΡΠ΅Π²Π°Π³ΠΈ Π²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ:
- ΠΠΈ Π³Π°ΡΠ°Π½ΡΡΡΠΌΠΎ ΡΡΠΊΠ°Π²Ρ ΠΏΡΠΎΡΠΊΡΠΈ Π² ΠΎΠ±Π»Π°ΡΡΡ Π²ΡΠΉΡΡΠΊΠΎΠ²ΠΎΡ ΡΠ΅Ρ Π½ΡΠΊΠΈ ΡΠ° ΡΠ°Π΄ΡΠΎΠ΅Π»Π΅ΠΊΡΡΠΎΠ½ΡΠΊΠΈ.
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΠΏΠ»ΠΈΠ²Π°ΡΠΈ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡ Ρ ΠΏΡΠΎΡΠ΅ΡΠΈ.
- ΠΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½Ρ ΠΏΠ»Π°ΡΡ;
- ΠΠΈ Π½Π°Π΄Π°ΡΠΌΠΎ Π±ΡΠΎΠ½ΡΠ²Π°Π½Π½Ρ!
Π―ΠΊΡΠΎ Π²ΠΈ Π³ΠΎΡΠΎΠ²Ρ ΠΏΡΠΈΡΠ΄Π½Π°ΡΠΈΡΡ Π΄ΠΎ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ ΡΠ° Π²Π½Π΅ΡΡΠΈ ΡΠ²ΡΠΉ Π²Π½Π΅ΡΠΎΠΊ Ρ ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ Π²ΡΠΉΡΡΠΊΠΎΠ²ΠΈΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ, Π½Π°Π΄ΡΠΈΠ»Π°ΠΉΡΠ΅ ΡΠ΅Π·ΡΠΌΠ΅ Π΄Π»Ρ ΡΡΠ°ΡΡΡ Ρ Π²ΡΠ΄Π±ΠΎΡΡ.
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Β· 58 views Β· 3 applications Β· 11d
Data Engineer
Office Work Β· Ukraine (Kyiv) Β· Product Β· 3 years of experience Β· English - None MilTech πͺΠΠΈ ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° miltech ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΊΠ° ΡΡΠ²ΠΎΡΡΡ Π²ΠΈΡΠΎΠΊΠΎΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠ½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ Π΄Π»Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ Π·Π°Π²Π΄Π°Π½Ρ Ρ ΡΡΠ΅ΡΡ ΠΎΠ±ΠΎΡΠΎΠ½ΠΈ, Π²ΡΠ΄ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΡ Π΄ΠΎ ΡΠ΅ΡΡΠΉΠ½ΠΎΠ³ΠΎ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°. ΠΡΠ½ΠΎΠ²Π½ΠΈΠΉ ΡΠΎΠΊΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ - ΡΠ΅ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π½Π°ΠΉΠΊΡΠ°ΡΠΎΡ ΡΠΊΠΎΡΡΡ ΡΠ° Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡ,...ΠΠΈ ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° miltech ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΊΠ° ΡΡΠ²ΠΎΡΡΡ Π²ΠΈΡΠΎΠΊΠΎΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡΠ½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ Π΄Π»Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ Π·Π°Π²Π΄Π°Π½Ρ Ρ ΡΡΠ΅ΡΡ ΠΎΠ±ΠΎΡΠΎΠ½ΠΈ, Π²ΡΠ΄ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΡ Π΄ΠΎ ΡΠ΅ΡΡΠΉΠ½ΠΎΠ³ΠΎ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°.
ΠΡΠ½ΠΎΠ²Π½ΠΈΠΉ ΡΠΎΠΊΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ - ΡΠ΅ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π½Π°ΠΉΠΊΡΠ°ΡΠΎΡ ΡΠΊΠΎΡΡΡ ΡΠ° Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡ, ΡΠ»ΡΡ ΠΎΠΌ ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ Π°ΠΏΠ°ΡΠ°ΡΠ½ΠΈΡ ΡΠ° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ½ΠΈΡ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡΠ² ΠΠΠΠ.
ΠΠ°ΠΉΠ±ΡΡΠ½Ρ ΠΎΠ±ΠΎΠ²'ΡΠ·ΠΊΠΈ:- Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° ΠΌΠ°ΡΡΡΠ°Π±ΠΎΠ²Π°Π½ΠΎΡ Π°ΡΡ ΡΡΠ΅ΠΊΡΡΡΠΈ ΡΡ ΠΎΠ²ΠΈΡΠ° Π΄Π°Π½ΠΈΡ Π· ΡΡΠ°Ρ ΡΠ²Π°Π½Π½ΡΠΌ Π½Π°Π΄ΡΠΉΠ½ΠΎΡΡΡ ΡΠ° Π·ΡΠΎΡΡΠ°Π½Π½Ρ Π½Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Ρ.
- ΠΠΎΠ±ΡΠ΄ΠΎΠ²Π° ΡΠ° ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° ΠΏΡΠΎΡΠ΅ΡΡΠ² Π·Π±ΠΎΡΡ, ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΡΡ ΠΉ Π΄ΠΎΡΡΠ°Π²ΠΊΠΈ Π΄Π°Π½ΠΈΡ ΡΠ· ΡΡΠ·Π½ΠΎΡΡΠ΄Π½ΠΈΡ Π΄ΠΆΠ΅ΡΠ΅Π» (Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , Π·ΠΎΠ²Π½ΡΡΠ½Ρ ΡΠ΅ΡΠ²ΡΡΠΈ, ΡΠ°Π±Π»ΠΈΡΡ, ΡΠ°ΠΉΠ»ΠΈ).
- ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΡΡ ΡΠ° ΠΎΡΠΊΠ΅ΡΡΡΠ°ΡΡΡ ΠΏΠΎΡΠΎΠΊΡΠ² Π΄Π°Π½ΠΈΡ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ ΡΡΡΠ°ΡΠ½ΠΈΡ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ².
- ΠΠΎΠ½ΡΡΠΎΠ»Ρ ΡΠΊΠΎΡΡΡ Π΄Π°Π½ΠΈΡ , Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΠΏΠ΅ΡΠ΅Π²ΡΡΠΎΠΊ ΡΠ° ΡΠΈΡΡΠ΅ΠΌ ΡΠΏΠΎΡΡΠ΅ΡΠ΅ΠΆΠ΅Π½Π½Ρ Π·Π° ΡΡΠ°Π½ΠΎΠΌ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ².
- Π£ΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ ΠΌΠ΅ΡΠ°Π΄Π°Π½ΠΈΠΌΠΈ, ΡΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ Π΄ΠΎΡΡΡΠΏΠ°ΠΌΠΈ ΡΠ° Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡΠ²Π°Π½Π½Ρ ΡΠ΅Ρ Π½ΡΡΠ½ΠΈΡ ΡΡΡΠ΅Π½Ρ.
- Π’ΡΡΠ½Π° Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ Π· Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΠΌΠΈ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ ΡΠ° Π±ΡΠ·Π½Π΅Ρ-ΡΡΠ΅ΠΉΠΊΡ
ΠΎΠ»Π΄Π΅ΡΠ°ΠΌΠΈ, ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠ° ΡΡΠ½ΡΡΡΠΈΡ
Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ
ΡΡΡΠ΅Π½Ρ Ρ Π²ΡΡΡΠΈΠ½.
ΠΠΈΠΌΠΎΠ³ΠΈ Π΄ΠΎ ΡΠΏΠ΅ΡΡΠ°Π»ΡΡΡΠ°:
- ΠΠΎΡΠ²ΡΠ΄ Ρ ΡΡΠ΅ΡΡ data engineering ΠΏΠΎΠ½Π°Π΄ 3 ΡΠΎΠΊΠΈ.
- ΠΡΠ΄ΠΌΡΠ½Π½Π΅ Π·Π½Π°Π½Π½Ρ ΡΠ΅Π»ΡΡΡΠΉΠ½ΠΈΡ Π±Π°Π· Π΄Π°Π½ΠΈΡ ΡΠ° Π²ΠΌΡΠ½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π·Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΠΌΠΈ SQL-Π·Π°ΠΏΠΈΡΠ°ΠΌΠΈ.
- ΠΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Apache Airflow Π΄Π»Ρ ΠΊΠ΅ΡΡΠ²Π°Π½Π½Ρ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ , Π²ΠΊΠ»ΡΡΠ½ΠΎ Π· ΠΏΠΎΠ²Π½ΠΈΠΌΠΈ ΡΠ° ΡΠ½ΠΊΡΠ΅ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΈΠΌΠΈ Π·Π°Π²Π°Π½ΡΠ°ΠΆΠ΅Π½Π½ΡΠΌΠΈ.
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π²Π½ΡΡΡΡΡΠ½ΡΠΎΡ Π»ΠΎΠ³ΡΠΊΠΈ Π‘Π£ΠΠ: Π½Π°ΠΏΠΈΡΠ°Π½Π½Ρ ΡΠ΅ΡΠ²Π΅ΡΠ½ΠΎΡ Π»ΠΎΠ³ΡΠΊΠΈ, Π°Π½Π°Π»ΡΠ· ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΠΎΡΡΡ, ΡΠΎΠ±ΠΎΡΠ° Π· ΡΠ½Π΄Π΅ΠΊΡΠ°ΠΌΠΈ ΡΠ° ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ Π·Π°ΠΏΠΈΡΡΠ².
- ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Python ΡΠΊ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡ Π΄Π»Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ ΡΠ° ΡΠ½ΡΠ΅Π³ΡΠ°ΡΡΡ Π΄Π°Π½ΠΈΡ ; Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ Π΄Π»Ρ Π°Π½Π°Π»ΡΠ·Ρ, HTTP-Π·Π°ΠΏΠΈΡΡΠ² ΡΠ° ΠΏΡΠ΄ΠΊΠ»ΡΡΠ΅Π½Π½Ρ Π΄ΠΎ ΠΠ.
- ΠΠ°ΠΊΡΠ½ΡΠ΅Π½Π° Π²ΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° Ρ ΡΡΠ΅ΡΡ ΠΠ’, ΠΏΡΠΈΠΊΠ»Π°Π΄Π½ΠΎΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ Π°Π±ΠΎ ΡΡΠΌΡΠΆΠ½ΠΈΡ
Π½Π°ΠΏΡΡΠΌΡΠ².
ΠΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΎΡ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ Π±ΡΠ΄Π΅: - ΠΠ°Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΊΠΎΡΠ΅ΠΊΡΠ½ΠΎΡΡΡ ΡΠ° ΠΏΠΎΠ²Π½ΠΎΡΠΈ Π΄Π°Π½ΠΈΡ Π½Π° ΡΡΠ·Π½ΠΈΡ Π΅ΡΠ°ΠΏΠ°Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ.
- Π ΠΎΠ±ΠΎΡΠ° Π· ΠΊΠΎΠ½ΡΠ΅ΠΉΠ½Π΅ΡΠ°ΠΌΠΈ ΡΠ° ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ ΠΊΠ΅ΡΡΠ²Π°Π½Π½Ρ Π½ΠΈΠΌΠΈ Ρ ΠΏΡΠΎΠ΄Π°ΠΊΡΠ½-ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΡ.
- ΠΠΎΡΠ²ΡΠ΄ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Ρ ΠΌΠ°ΡΠ½ΠΎΡ ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠΈ Π΄Π»Ρ Π·Π±Π΅ΡΡΠ³Π°Π½Π½Ρ ΡΠ° ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π²Π΅Π»ΠΈΠΊΠΈΡ ΠΎΠ±ΡΡΠ³ΡΠ² ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΡ.
ΠΠΎΠΌΠΏΠ°Π½ΡΡ ΠΏΡΠΎΠΏΠΎΠ½ΡΡ:- ΠΡΡΡΡΠΉΠ½Π΅ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ.
- Π‘ΡΡΠ°ΡΠ½ΠΈΠΉ ΠΎΡΡΡ Ρ ΠΠΈΡΠ²Ρ.
- Π©ΠΎΡΡΡΠ½Π° ΠΎΠΏΠ»Π°ΡΡΠ²Π°Π½Π° Π²ΡΠ΄ΠΏΡΡΡΠΊΠ° β 25 ΠΊΠ°Π»Π΅Π½Π΄Π°ΡΠ½ΠΈΡ Π΄Π½Ρ.
- ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ ΠΏΡΡΠ»Ρ Π·Π°Π²Π΅ΡΡΠ΅Π½Π½Ρ Π²ΠΈΠΏΡΠΎΠ±ΡΠ²Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΡΠΌΡΠ½Ρ.
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π΄ΠΎΠ»ΡΡΠΈΡΠΈΡΡ Π΄ΠΎ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΡΡΠ°ΡΠ½ΠΈΡ ΡΠΎΠ·ΡΠΎΠ±ΠΎΠΊ ΡΠ° Π²ΠΈΡΡΡΠ΅Π½Π½Ρ Π½Π΅ΡΡΠΈΠ²ΡΠ°Π»ΡΠ½ΠΈΡ Π·Π°Π²Π΄Π°Π½Ρ.
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΎΡΡΠΈΠΌΠ°ΡΠΈ Π±ΡΠΎΠ½ΡΠ²Π°Π½Π½Ρ, Π·Π° Π½Π°ΡΠ²Π½ΠΎΡΡΡ Π²ΡΠΉΡΡΠΊΠΎΠ²ΠΎ-ΠΎΠ±Π»ΡΠΊΠΎΠ²ΠΈΡ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΡΠ².
- ΠΠΌΠΎΡΠΈΠ²ΠΎΠ²Π°Π½Π° ΠΊΠΎΠΌΠ°Π½Π΄Π° ΠΎΠ΄Π½ΠΎΠ΄ΡΠΌΡΡΠ², ΡΠΊΠΈΡ ΠΎΠ±'ΡΠ΄Π½ΡΡ ΡΠΏΡΠ»ΡΠ½Π° ΠΌΠ΅ΡΠ°.
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