Jobs

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.

About the role:
We are looking for a Data Engineer (NLP-Focused) to build and optimize the data pipelines that fuel the Ukrainian LLM and NLP initiatives. In this role, you will design robust ETL/ELT processes to collect, process, and manage large-scale text and metadata, enabling the 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 the datasets and infrastructure are reliable, scalable, and tailored to the needs of training and evaluating NLP models in a Ukrainian language context.

Requirements:
- Education & Experience: 3+ years of experience as a Data Engineer or in a similar role, building data-intensive pipelines or platforms. A Bachelor’s or Master’s 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 the project’s 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 the 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.

Responsibilities:
- 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 the 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.

The company offers:
- Competitive salary.
- Equity options in a fast-growing AI company.
- Remote-friendly work culture.
- Opportunity to shape a product at the intersection of AI and human productivity.
- Work with a passionate, senior team building cutting-edge tech for real-world business use.

We are currently seeking a Solution Architect who specializes in data-driven projects to become a part of our Data Practice team in Ukraine.

Responsibilities

• Architect data analytics solutions by leveraging the big data technology stack
• Develop and present detailed technical solution architecture documents
• Collaborate with business stakeholders to define solution requirements and explore case studies/scenarios for future solutions
• Perform solution architecture reviews/audits, compute and present ROI
• Manage the implementation of solutions from setting project requirements and objectives to the solution “go-live”
• Engage in the entire spectrum of pre-sale activities, including direct communication with customers, RFP processing, crafting implementation proposals, and solution architecture presentations to clients, as well as participation in technical discussions with client representatives
• Construct and adhere to a personal education plan in technology stack and solution architecture
• Develop a robust understanding of industry trends and best practices
• Participate in the acquisition of new clients to expand EPAM’s business in the big data sector

Requirements

• Minimum of 7 years' experience required
• Proficiency in hands-on roles as a Big Data Architect with a strong design/development background in Java, Scala, or Python
• Background in delivering data analytics projects and architecture guidelines
• Skills in big data solutions, both on-premises and on cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud
• Production project experience with at least one big data technology is essential
• Batch processing expertise: Hadoop, MapReduce/Spark/Hive
• Understanding of NoSQL databases: Cassandra, HBase, Accumulo, Kudu
• Familiarity with Agile development methodology, particularly Scrum
• Competency in client communication and pre-sales business-consulting to large enterprise environments
• Experience within a consulting firm and pre-sales backgrounds are highly desirable
• Upper-Intermediate level in English, both spoken and written (B2+)

We are seeking an experienced Data Engineer with a strong background in healthcare data integration, cloud platforms, and Salesforce data ecosystems. The ideal candidate will have proven expertise in building and optimizing scalable, secure, and high-performance data pipelines that power analytics, reporting, and patient engagement workflows.

Experience Requirements

Advanced Data Integration (5+ years):

• Strong experience integrating data from healthcare systems, Salesforce, and cloud-based sources.
• Expertise in managing complex data pipelines for large-scale data ingestion and transformation.
• Hands-on experience integrating Salesforce data using MuleSoft, Salesforce APIs, and Data Loader.
• Deep understanding of how Salesforce data supports patient engagement, clinical workflows, and reporting.

Cloud Platform Expertise (5+ years):

• Proven experience with AWS services (S3, Redshift, Glue, Lambda, Athena, EC2) for data storage, processing, and orchestration.
• Experience scaling cloud infrastructure to manage large and sensitive healthcare datasets.

Healthcare Data Experience (3+ years):

• Strong background working with healthcare data (clinical data, patient records, lab results).
• Familiarity with healthcare data integration and regulatory standards such as HIPAA.
• Bachelor’s or Master’s Degree in Computer Science, Data Engineering, Health Informatics, or a related field.
• Equivalent practical experience may also be considered.

Required Skills:

• Expertise with AWS data services (S3, Redshift, Glue, Lambda, Athena, EMR).
• Ability to architect and optimize data pipelines for performance, scalability, and reliability.
• Proficiency in integrating Salesforce data via APIs.
• Strong ETL/ELT experience with AWS Glue, Apache Airflow, or custom Python scripts.
• Knowledge of healthcare data security standards (HIPAA, HITECH), encryption techniques, and secure data transfer.
• Hands-on experience with AWS Redshift or Snowflake for building high-performance data warehouses, enabling efficient querying and BI accessibility.
• Proven collaboration with BI and analytics teams to translate business requirements into technical solutions and ensure data supports actionable insights.

We offer:

• Remote work;
• Flexible schedule and ability to manage your working hours;
• Competitive salary;
• Working in a team of skilled and experienced specialists;
• Opportunities for professional development.

Jooble is a global technology company. Our main product jooble.org is an international job search website in 67 countries that aggregates thousands of job openings from various sources on a single page. We are ranked among the TOP-10 most visited websites in the Jobs and Employment segment worldwide. Since 2006, we’ve grown from a small startup founded by two students into a major player in the online recruitment market with 300+ professionals. Where others see challenges, we create opportunities.

What You'll Be Doing

• Design & Build Pipelines: Develop, and maintain robust and scalable ETL/ELT pipelines, moving data from diverse sources into our data warehouse.
• Ensure Data Quality & Observability: Implement a comprehensive data observability strategy, including automated quality checks, monitoring, and lineage tracking to ensure data is accurate and trustworthy.
• Optimize & Automate: Write clean, efficient code to automate data processing and continuously optimize our data storage strategies and query performance.
• Govern & Document: Contribute to our data governance practices and maintain clear documentation for data processes, models, and architecture in our data catalog.
   

What We're Looking For Core Requirements

• Experience: 1-3 years of hands-on experience in a data engineering role.
• Ukrainian proficiency level: Upper Intermediate and higher (spoken and written).
• Core Languages: Strong proficiency in SQL (including complex queries and optimization) and Python for data processing.
• Databases: Practical experience with relational databases, specifically PostgreSQL and MSSQL.
• ETL/ELT: Proven experience designing and building pipelines using modern data orchestrators like Airflow or Dagster.
• Data Modeling: A solid understanding of data warehousing concepts and data modeling techniques (e.g., dimensional modeling).
• Ukrainian proficiency level: Upper Intermediate and higher (spoken and written)

Bonus Points (Strongly Desired)
 

• Streaming Data: Hands-on experience with streaming technologies like Kafka, Debezium, or message queues like RabbitMQ.
• Specialized Databases: Experience with MPP databases (Greenplum/CloudberryDB) or columnar stores (ClickHouse).
• Modern Data Stack: Familiarity with tools like dbt, Docker.
• Basic knowledge of a cloud platform like AWS, GCP, or Azure.
• A demonstrable interest in the fields of AI and Machine Learning.

Our Tech Stack Includes

• Observability & BI: DataHub, Grafana, Metabase
• Languages: Python, SQL
• Databases: PostgreSQL, MSSQL, ClickHouse, Greenplum/CloudberryDB
• Orchestration: Airflow, Dagster
• Streaming & Messaging: Kafka, Debezium, RabbitMQ

Why You'll Love Working at Jooble

Flexible Work Environment
We offer a hybrid format in Kyiv and remote options worldwide. Start your 8-hour workday between 8:00 and 10:00 AM Kyiv time, ensuring collaboration across our team in 20+ countries. We provide all the equipment you need for productivity and comfort, whether remotely or in the office.

Growth and Development
We invest in your future with an individual education budget covering soft and hard skills. Career opportunities and regular performance reviews support your growth from entry-level to leadership roles.

Healthcare and Well-being
We offer health insurance after three months, plus financial support for medical expenses abroad. Our mental health benefits include access to psychological consultations and 50% reimbursement for therapy sessions.

Time Off
Enjoy 24 vacation days, 20 paid sick days, 4 extra sick days without a medical certificate, and 6 recharge days. Take the time you need and return refreshed!

Our culture

We embrace a product mindset, continuously innovating and improving our services to meet the needs of our users. We cultivate a workplace that values support, respect, honesty, and the free exchange of ideas. Experience an environment where "stronger together" is more than just a phrase — it's how we operate, fostering creativity and growth.

Supporting Ukraine

Since the beginning of the war, Jooble has been actively supporting and organizing fundraisers to aid our country. Many of our colleagues are bravely serving on the front lines or volunteering, and we couldn’t be prouder of their dedication and efforts. We committed to supporting our nation in any way we can.

Ready to Make an Impact? If you’re passionate about this opportunity and want to join our team, please send us your CV. Our recruiter will be in touch with you soon.

Who We Are:
Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.  

About the Product:
Our platform provides organizations with real-time visibility and control over their digital environments, enabling IT teams to detect, diagnose, and resolve issues before they impact employees. It integrates multiple products into a single, unified experience—leveraging AI-driven automation, intelligent data processing, and scalable architecture to enhance productivity across global workplaces. The DEX Platform team builds the core infrastructure that powers these capabilities, delivering high-performance backend services and advanced data pipelines at scale.

About the Role:
We are looking for an experienced Senior Backend Data Engineer to join our advanced employee experience monitoring and optimization platform and take a leading role in building the next generation of our data infrastructure. This role involves designing and implementing large-scale, real-time data pipelines and backend services that support both operational workflows and AI-driven insights.

You will work end-to-end—covering architecture, development, deployment, and ongoing production monitoring—while collaborating closely with backend, AI, and data specialists to ensure high performance, scalability, and reliability.

Key Responsibilities:

• Design, develop, and maintain robust backend services and data processing pipelines for large-scale, real-time environments.
• Build and optimize streaming solutions using technologies like Kafka, Flink, and other stream-processing frameworks.
• Own the full lifecycle of services: architecture, implementation, deployment, monitoring, and scaling.
• Collaborate with cross-functional teams, including backend engineers, AI developers, and data analysts, to deliver production-ready solutions.
• Ensure compliance, security, and observability for all data-related systems.
• Work with cloud infrastructure to design and deploy scalable solutions.
• Troubleshoot and resolve production issues with a focus on high availability and system resilience.

Required Competence and Skills:

• 5+ years of backend/data engineering experience.
• Strong proficiency in Node.js (NestJS/Express).
• Familiarity with Java (Vert.x or Spring) will be a big advantage.
• Solid understanding of microservices architecture and cloud platforms (Azure, AWS, or GCP).
• Hands-on experience with Kafka and streaming frameworks such as Kafka Streams, Flink, Spark, or Beam.
• Strong foundation in object-oriented design, design patterns, and clean code principles.
• Experience in production-aware environments, including monitoring, troubleshooting, and optimization.
• Comfortable designing, deploying, and maintaining backend services and data flows.
• Passion for continuous learning, experimenting with new technologies, and building reliable systems at scale.
• Strong product mind-set, open-mindedness and flexibility to work with different technologies as per company needs
• Excellent communication skills in English (Hebrew a plus).
• Team player with a positive attitude and a passion for delivering high-quality products.

Nice to have:

• Familiarity with AI-first development tools (e.g., GitHub Copilot, Cursor).
• Knowledge of Postgres, Redis, or ClickHouse.

Why Us?

We provide 20 days of vacation leave per calendar year (plus official national holidays of the country you are based in).

We provide full accounting and legal support in all countries in which we operate.

We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

We offer a highly competitive package with yearly performance and compensation reviews.

Who we are:
Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries. 

About the Product:
Our client is a leading SaaS company offering pricing optimization solutions for e-commerce businesses. Its advanced technology utilizes big data, machine learning, and AI to assist customers in optimizing their pricing strategies and maximizing their profits.

About the Role:
As a Senior Data Engineer, you will operate at the intersection of data engineering, software engineering, and system architecture. This is a high-impact, cross-functional role where you’ll take end-to-end ownership – from designing scalable infrastructure and writing robust, production-ready code to ensuring the reliability and performance of our systems in production. 

Key Responsibilities:

• Build and maintain ETL/ELT pipelines from APIs, Kafka, and databases.
• Design and manage Airflow DAGs that are modular and observable.
• Optimize our data lake architecture on S3 and Athena for performance and scalability.
• Develop and support real-time and event-driven data flows using Kafka and Spark.
• Implement monitoring, data validation, and alerting to ensure pipeline reliability.
• Expose clean and structured datasets for internal consumption via Athena or APIs.
• Collaborate with DevOps and the architect to evolve data infrastructure.

Required Competence and Skills:

• 5+ years of experience as a data engineer, software engineer, or similar role, with a proven track record of using data to drive business outcomes. 
• Strong Python skills, with experience building modular, testable, and production-ready code. 
• AWS Certified Data Analytics – Specialty or AWS Certified Big Data – Specialty certification (current or expired) 
• Solid understanding of Databases and SQL, ETL/ELT design, and distributed data processing.
• Experience with Airflow, Kafka, S3, Athena, Glue, and CI/CD practices.
• Excellent communication and collaboration skills, proactive approach.

Nice-to-Haves

• Experience with streaming technologies (Flink, Spark Streaming).
• Experience building internal tools, APIs, or SDKs.

Data Engineer
Middle+/Senior
English Upper-intermediate
Duration - long term
Client's Location - UK

Role Responsibilities:

• Build connectors to external partners to harvest the data
• Build custom functions to process the data
• Integrate data into Snowflake and other reporting tools
• Work cross teams and cross functions to provide good quality and speed of data
• Champion and challenge existing solution to improve and optimize them even further

Key Skills/What they'll be working on:
Design, build, and maintain ETL/ELT pipelines and batch/streaming workflows.
Integrate data from external APIs and internal systems into Snowflake and downstream tools.
Own critical parts of our Airflow-based orchestration layer and Kafka-based event streams.
Ensure data quality, reliability, and observability across our pipelines and platforms.
Build shared data tools and frameworks to support analytics and reporting use cases.
Partner closely with analysts, product managers, and other engineers to support data-driven decisions.

Role requirements:
3+ years of experience as a Data Engineer or Software Engineer working on data infrastructure.
Strong Python skills and hands-on experience with SQL and Snowflake.
Experience with modern orchestration tools like Airflow and data streaming platforms like Kafka.
Understanding of data modeling, governance, and performance tuning in warehouse environments.
Ability to work independently and prioritize across multiple stakeholders and systems.
Comfort operating in a cloud-native environment (e.g., AWS, Terraform, Docker).

Python side:
must have is experience in pulling and managing data from APIs
nice to have would be web scraping via browser automation (playwright / selenium / puppeteer for example)

Details:
This is for E2E Project, which is collecting player level data for brands, which helps to track revenue on various pages/brands and better understand how users are using products and what's trending. The vision for this is to become the central source of truth for user journey insights, empowering company to make smarter, faster, and more impactful decisions that drive commercial growth and product innovation.

Job Description

Familiarity with Google Cloud Platform, including BigQuery, Cloud Composer(Airflow), Compute Engine, and Monitoring.
Strong proficiency in Python, including experience in building, testing, and deploying robust ETL/ELT pipelines in production
Experience developing data quality frameworks, including automated tests, cross-dataset validations, and anomaly detection across diverse data assets
Advanced SQL skills, including data modeling (star/snowflake/3NF), optimization, and writing performant queries tailored to BigQuery’s slot-based execution model
Solid working knowledge of DBT, including incremental models, testing, documentation, and advanced features like custom macros
Demonstrated ability to optimize BigQuery workloads through partitioning, clustering, materialized views, and cost-aware development practices
Experience working with ETL orchestration tools, ideally Cloud Composer or similar frameworks (Kestra, Dagster,etc.)
Hands-on experience consuming and managing APIs for data extraction.
Exposure to Site Reliability Engineering (SRE) best practices, including ticket triage, incident management, and documenting runbooks/SOPs
Familiar with Git and modern software development workflows, including pull requests and code reviews
Comfortable working in an agile team environment, with the ability to multitask and reprioritize based on changing project needs
Clear and effective communication skills, with the ability to engage technical and non-technical stakeholders alike

Job Responsibilities

Designing, building, and maintaining scalable, reliable data pipelines using Python,SQL, DBT, and Google Cloud Platform (GCP) services like BigQuery and Cloud Composer.
Contributing to the development and evolution of our data quality framework, ensuring robust automated testing and cross-dataset validation across all critical data assets
Writing and optimizing advanced SQL to power data models, analytics, and reporting pipelines, with a focus on performance and efficiency within BigQuery
Developing and maintaining DBT models, including testing, documentation, incremental loading strategies, and the creation of reusable macros
Supporting day-to-day incident and ticket resolution, including root cause analysis and documentation of runbooks/SOPs to drive platform reliability
Working with APIs and external data sources to extract, normalize, and integrate new datasets into the platform
Participating in agile ceremonies (stand-ups, retros, planning), contributing to sprint goals and helping support the wider team objectives
Actively contributing to code reviews, documentation, and peer learning—helping to raise engineering standards and share knowledge within the team
Monitoring system health and pipeline performance using GCP-native tools and dashboards, and proactively identifying opportunities for cost or performance optimization

Bonus points for

GCP Data Engineer certification
Prior experience in media, marketing, or digital advertising analytics
Experience implementing data quality frameworks and governance tools
Familiarity with cost optimization strategies for BigQuery and other GCP services

Department/Project Description

WPP is transforming its global data infrastructure to deliver faster, more scalable, and more intelligent analytics capabilities. As part of this journey, we're hiring a Technical Lead, Data Engineering to manage the technical delivery and operational resilience of our core data platform.

This role sits at the intersection of engineering leadership, platform reliability, and architecture — helping us ensure that data flows are healthy, scalable, and ready for production. You’ll work across project delivery, production support, and platform enhancement initiatives while mentoring a growing team of engineers.

Job Description

- 3+ years of intermediate to advanced SQL

- 3+ years of python development (intermediate level is fine: Pandas, Numpy, boto3, seaborn, requests, unittest)

- Experience building ETLs, preferably in python

- Experience with data tools (ex.: Airflow, Grafana, AWS Glue, AWS Athena)

- Excellent understanding of database design

- Cloud expereince (AWS S3, Lambda, or alternatives)

- Agile SDLC knowledge
- Detail oriented
- Data-focused
- Strong verbal/written communication and data presentation skills, including an ability to effectively communicate with both business and technical teams
- An ability and interest in working in a fast-paced and rapidly changing environment
- Be self-driven and show ability to deliver on ambiguous projects with incomplete or dirty data

Would be a plus:
- Understanding of basic SVOD store purchase workflows
- Background in supporting data scientists in conducting data analysis / modelling to support business decision making

- Experience in supervising subordinate staff

Job Responsibilities

- Data analysis, auditing, statistical analysis
- ETL buildouts for data reconciliation
- Creation of automatically-running audit tools
- Interactive log auditing to look for potential data problems
- Help in troubleshooting customer support team cases
- Troubleshooting and analyzing subscriber reporting issues:
      Answer management questions related to subscriber count trends
      App purchase workflow issues
      Audit/reconcile store subscriptions vs userdb

Department/Project Description

Customer is one of the biggest companies on the market of home entertainment consumer electronics devices that strives to provide their clients with high-quality products and services.

This position collaborates with a geographically diverse team to develop, deliver, and maintain systems for digital subscription and transactional products across the Customer’ SVOD portfolio.

We’re hiring a Data Engineer (Python / PySpark), who brings his professional skills and passion to deliver awesome enterprise solutions.

✅ Knowledge of English at least Upper-Intermediate level.

• Location: Lviv, Ukraine (on-site / hybrid)
• Workplace: Candidate must be based in Lviv or willing to relocate before start
• Note: This position requires the employee to work on-site in Lviv

Responsibilities:

• Design, build, and maintain ETL pipelines for large-scale data processing
• Develop batch and streaming data workflows using Python and PySpark
• Work with cloud data platforms such as AWS Glue, EMR, S3, and AWS Data Pipeline
• Integrate and manage messaging systems like Kafka and RabbitMQ
• Develop and maintain solutions using Hadoop ecosystem components: HDFS, Hive
• Optimize data storage and query performance in relational databases (PostgreSQL, Redshift)
• Containerize data workflows using Docker
• Orchestrate workflows with Airflow
• Implement CI/CD pipelines for data workflows and maintain version control (Git)
• Monitor data pipelines and system performance using Grafana and logging tools
• Ensure data security and access control: encryption, IAM, and compliance best practices
•  

Requirements:

• 4–5+ years experience in Data Engineering
• Strong proficiency in Python and PySpark
• Hands-on experience with ETL pipelines and data modeling
• Knowledge of cloud data services (AWS Glue, EMR, S3, Data Pipeline)
• Experience with messaging systems: Kafka, RabbitMQ
• Familiarity with Hadoop ecosystem: HDFS, Hive
• Strong SQL skills (PostgreSQL, Redshift)
• Experience with Docker and workflow orchestration (Airflow)
• Knowledge of CI/CD and version control (Git)
• Monitoring and logging experience (Grafana)
• Understanding of data security, encryption, and access control
• Analytical mindset and strong problem-solving skills
• Upper-Intermediate English or higher

Nice to Have:

• Experience with multi-cloud environments or hybrid infrastructures
• Familiarity with big data performance tuning (partitioning, memory optimization)
• Experience with real-time streaming data processing
• Knowledge of data governance and compliance standards

What we can offer:

• Full-time flexible working schedule;
• Comfortable, cosy, and well-equipped office;
• Modern working place MacBookPro;
• 18 business days of paid vacation / 20 paid sick leaves / public holidays in Ukraine.
• English lessons

Mindy Support is a global provider of data collection, annotation, and curation services, partnering with leading global technology companies. 

Our mission is to deliver high-quality, ethically sourced data that fuels the next generation of AI/ML products and solutions. We combine people, process, and technology to deliver reliable data solutions at a large scale.

Role Overview
 

We are seeking an experienced Data Engineer to design, build, and optimize data pipelines and infrastructure, supporting our annotation and curation workflows. 

Beyond engineering excellence, this role requires strong client-facing skills. You will engage directly with enterprise-grade clients to gather requirements, analyze use cases, and ensure project success. You will also contribute to our internal innovation initiatives, helping shape and extend our data services offering.
 

This role is ideal for a technical leader who can and wants to act at the intersection of data engineering, client engagement, and innovation.

For the right candidate, this position offers a clear growth path to a technical leadership role at the organizational level.

Key Responsibilities
 

• Data Engineering & Infrastructure
  
   
  • Design, build, and maintain robust ETL/ELT pipelines for diverse data sources (text, image, audio, video) both on our and clients’ infrastructures. 
  • Develop scalable data processing systems to support data collection/generation, curation, and labeling workflows.
  • Ensure data quality, security, and compliance across projects.
  • Optimize storage, retrieval, and transformation processes for performance and cost efficiency.
    
     
• Client Engagement & Project Coordination
  
   
  • Participate in requirements elicitation, translating client needs into technical solutions.
  • Collaborate with our project managers and operations team to align engineering solutions with project goals.
  • Help clients to build proper Data Governance frameworks
  • Communicate technical aspects, risks, and dependencies to clients and internal stakeholders.
    
     
• Leadership & Innovation
  
   
  • Provide technical leadership to our cross-functional team.
  • Drive innovation initiatives to extend our portfolio of data services (e.g., automation tools, quality assurance workflows, synthetic data pipelines, advanced analytics etc.).
  • Stay up to date with industry trends in Data Engineering, Data Science and overall developments of Generative AI/Agentic AI/Physical AI

Qualifications

• 5+ years in data engineering, with at least 2 years in a senior or lead role;
• Strong proficiency in Python, SQL, and one or more big data frameworks (Spark, Beam, Flink, etc.);
• Experience with cloud platforms (preferably AWS) and data warehouse solutions (BigQuery, Snowflake, Redshift or similar);
• Knowledge of data modeling, pipeline orchestration (Airflow, Prefect, Dagster), and API integration;
• Familiarity with unstructured data processing (text, image, audio, video);
• Advanced to Fluent English;
• Excellent communication and stakeholder management abilities;
• Strong analytical and problem-solving mindset;
• Proven track record in collaborating with clients and cross-functional teams.
  
   

Would be an advantage:

• Experience in data annotation field
• Prior involvement in innovation or R&D initiatives
• Relevant professional certifications
• Experience working with American Big Tech (Apple / Google / Amazon / Meta / Microsoft / NVIDIA / OpenAI / Anthropic / Palantir or similar companies)
  
   

What We Offer

• Opportunity to work with leading global technology companies shaping the future of AI
• A dynamic environment that values innovation, experimentation, mutual trust and respect
• Career growth pathways into company-wide technical leadership
• Competitive compensation and benefits package
• Flexible remote work arrangement
   

The CHI Software team is not standing still. We love our job and give it one hundred percent of us! Every new project is a challenge that we face successfully. The only thing that can stop us is… Wait, it’s nothing! The number of projects is growing, and with them, our team too. And now we need a Middle/Senior Data Engineer.
 

Requirements:

• 4+ years of experience as a Data Engineer;
• Completed Data Engineering Professional certification;
• Minimum 2 projects delivered with hands-on experience in development on Databricks;
• Experience in Apache Spark Programming with Databricks;
• Experience with Azure cloud;
• Upper-Intermediate English level.

Job Type: Full-time, Long-term

Location: EU / LATAM

English: Upper-Intermediate

Start: ASAP

About the Project

We are building a complete end-to-end Data Product in Databricks that enables the analysis and visualization of energy market price data, linking it directly to the client’s internal asset portfolio.

The platform will provide:

• Analysis of price data across specific timestamps.
• Connection between market prices and internal asset performance.
• Insights into revenue and profit per asset.
• Dashboards and reports for decision-making.

Responsibilities

• Design and deliver a modern data infrastructure in Azure + Databricks.
• Build scalable, reliable, and reusable data pipelines.
• Integrate price data with internal asset data.
• Enable visualization of results via dashboards/reports.
• Ensure data harmonization, quality, integration, and security.
• Collaborate in an agile, cross-functional team to support data strategy initiatives.

Must-Have Skills

• Strong expertise in Python.
• Proven hands-on experience with Azure (Data Engineering stack).
• Databricks (end-to-end pipeline development & optimization).
• Scalable and secure data architecture design.
• Experience in data pipeline development (ETL/ELT).
• Knowledge of data warehousing, data modeling, and relational databases.
• Strong analytical and problem-solving mindset.

Nice-to-Have

• Experience in the energy sector or related environments.

Role Purpose

This role is critical in building and maintaining a modern data infrastructure that supports Renewables’ mission to lead the energy transition. You will be responsible for designing and delivering the Price Data Product, connecting market prices with asset performance across the entire fleet.

Key Responsibilities

• Design, develop, and maintain scalable data models to support analytics and reporting needs
• Build, monitor, and optimize ETL/ELT pipelines using best practices in data transformation and automation
• Collaborate with BI and analytics teams on data requirements
• Ensure data integrity and consistency via automated data tests 
• Proactively suggest data improvements, reporting ideas

Required Qualifications

• 3+ years of experience in analytics engineering, data engineering, or a related field
• Advanced proficiency in SQL, with experience in writing efficient data modeling queries
• Hands-on experience with modern data transformation frameworks (e.g. dbt, Dataform, or similar)
• Strong understanding of data warehousing principles and data architecture best practices
• Familiarity with ETL/ELT methodologies and workflow orchestration tools
• Experience working with cloud-based data warehouses and databases (Snowflake, PostgreSQL, Redshift, or similar)
• Knowledge of BI tools (Power BI, Tableau, Looker, or similar)
• Basic programming skills in Python or another scripting language for automation
• Solid understanding of data governance, lineage, and security best practices
• Experience with Git-based version control and CI/CD workflows for data transformations

Preferred Qualifications

• Deep understanding of data warehouse concepts and database maintenance
• Background in business intelligence, analytics, or software engineering
• Self-motivated and proactive, with the ability to independently uncover and solve problems

As a Junior Database Engineer, you will be responsible for maintaining and optimizing modern database systems. Your role will include backup management, replication monitoring, query optimization, and close collaboration with developers and DevOps engineers. This is an excellent opportunity for someone with a strong theoretical foundation in databases who wants to gain practical experience in real-world, high-performance environments.

Key Responsibilities

• Configure, monitor, and test backups; perform recovery checks.
• Monitor database replication and troubleshoot basic replication errors.
• Collect and analyze slow query statistics; participate in query optimization.
• Monitor database performance and apply necessary adjustments.
• Install and configure components of database architecture.
• Collaborate with developers and DevOps engineers to solve cross-team tasks.
• Participate in testing and deployment of new solutions.
• Write and debug scripts in Bash or Python to automate operations.
• Contribute to technical documentation.

Requirements

• Understanding of modern DBMS architecture (PostgreSQL, MySQL, MongoDB, etc.).
• Knowledge of relational data models and normalization principles.
• Understanding of ACID transaction properties.
• Experience installing and configuring at least one DBMS.
• Skills in writing SQL queries.
• Familiarity with monitoring systems (Prometheus, Grafana, PMM, etc.).
• Experience with Linux (Ubuntu/Debian).
• Ability to write simple automation scripts (Shell or Python).
• Strong sense of responsibility and attention to detail.

Nice-to-Have

• Technical degree or final-year student (IT, Cybersecurity, Mathematics, Informatics, etc.).
• Experience with high-load projects.
• Familiarity with Docker.
• Knowledge of replication (Master-Replica, WAL, GTID, MongoDB rs.replSet).
• Understanding of indexing and its impact on performance.
• Familiarity with cloud database services (AWS RDS, Azure Database, GCP Cloud SQL).

What We Offer

• Competitive salary based on experience and skills.
• Flexible working schedule (remote/hybrid).
• 17 paid vacation days and 14 paid sick leave.
• Mentorship and clear career growth path towards Senior Database Engineer.
• Access to courses, certifications, and conferences.
• Collaborative team and knowledge-sharing environment.
• International projects with modern tech stack.