Jobs Data Engineer

We are looking for a talented and experienced software engineer for the project which is related to the development of the brand-new databricks platform for unification of data streams from different streaming services for the global media and entertainment company that encompasses a diverse portfolio of television networks, film studios, theme parks and digital platforms.
The engineer will have the opportunity to be part of a strong R&D group that creates a cutting-edge solution designed to streamline and optimize business operations, provide seamless order processing, inventory management and real-time tracking by ensuring efficient and accurate order fulfillment.

Job Description

4+ years in data engineering
2+ years with Databricks
Strong Python and SQL
Hands-on experience with PySpark
Experience building production data pipelines with Delta Lake
Experience with orchestration tools such as Airflow, Databricks Workflows, or similar
Experience with APIs, ingestion jobs, and data transformation frameworks
Experience with testing, observability, and pipeline reliability
 

Job Responsibilities

Build and maintain scalable Databricks pipelines for customer and audience data
Integrate internal product, CRM, and third-party marketing datasets
Design data models for segmentation, activation, and reporting
Ensure data quality, privacy compliance, and operational reliability
Work with analysts, marketers, and platform teams on customer 360 use cases
Optimize Spark jobs for performance and cost

The company helps hotel chains like Hilton and Marriott transform front-desk staff into revenue generators through upselling training and analytics. Their IN-Gauge SaaS platform empowers frontline teams across 2,000+ properties in over 100 countries.

- Implement scaling of the case detection process (scanner)
- Split API into multiple API services
- Emulate pubsub/cloud run locally
- Implement scaling of the case detection process (scanner)
- Split API into multiple API services
- Emulate pubsub/cloud run locally
- Extract & process data using PySpark + S3 (Kafka/Kinesis — bonus)
- Write & optimize SQL on PostgreSQL/Redshift; use DynamoDB as data source/sink
- Run and debug ETL jobs on Glue or EMR; monitor via console/Spark UI

Requirements:

• Data engineering - 5 years
• AWS - 3 years (1 year latest project)
• Spark/PySpark - 2 years (1 year latest project)

Ask candidates the following questions:

• How do you use AI tools in your work as a data engineer?
• Can you share specific examples where it improved your efficiency or quality of work?
• What’s your experience with ELT & ETL? 

Important: ability to work in high load environments, we're looking for autonomous and proactive candidates

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 world’s 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 “data checks” and alerts to determine when the data is “bad”
• 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 end‑to‑end 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 self‑service access
• Design and deploy event‑driven architectures for near real‑time 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 large‑scale data infrastructure (hundreds of TBs of data, high‑throughput 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
• Bachelor’s degree in Computer Science, Engineering or related field required; Master’s 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 (production‑grade), 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 high‑frequency 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!

Responsibilities

Building both batch and streaming pipelines in production environments

Requirements (Must-have)

4-5 years of experience in Data Engineering

Strong experience with Apache Spark (including Structured Streaming)

Experience building both batch and streaming pipelines in production environments

Proven experience designing AWS-based data lake architectures (S3, EMR, Glue, Athena)

Experience with event streaming platforms such as Apache Kafka or Amazon Kinesis

Experience implementing lakehouse formats such as Delta Lake

Strong understanding of partitioning strategies and schema evolution

Experience using SparkUI and AWS CloudWatch for profiling and optimization

Strong understanding of Spark performance tuning (shuffle, skew, memory, partitioning)

Proven track record of cost optimization in AWS environments

Experience with Docker and CI/CD pipelines

Experience with Infrastructure as Code (Terraform, AWS CDK, or similar)

Familiarity with monitoring and observability practices

Nice to Have

Experience in the financial domain

Experience running Spark workloads on Kubernetes

Exposure to or interest in Large Language Models (LLMs) and AI integration

Experience implementing data quality frameworks or metadata/lineage systems

Hiring Process

Intro call

Technical discussion (focused on real experience)

Offer

Start: ASAP

On behalf of our client, we are looking for a Senior Data Engineer.

About the client: 

The company helps hotel chains like Hilton and Marriott transform front-desk staff into revenue generators through upselling training and analytics. Their IN-Gauge SaaS platform empowers frontline teams across 2,000+ properties in over 100 countries.
 

Key Responsibilities: 
- Implement scaling of the case detection process (scanner)
- Split API into multiple API services
- Emulate pubsub/cloud run locally
- Extract & process data using PySpark + S3 (Kafka/Kinesis — bonus)
- Write & optimize SQL on PostgreSQL/Redshift; use DynamoDB as data source/sink
- Run and debug ETL jobs on Glue or EMR; monitor via console/Spark UI

Company offers:

- Long-term employment with possibilities for professional growth

- Fully remote work

- Reasonably flexible schedule

- 15 days of paid vacation

- Regular performance reviews

🚀 Lead Data Engineer (EU)

We’re looking for a Lead Data Engineer to join a high-impact data transformation initiative focused on modernizing data pipelines and enabling scalable, future-ready data architecture.

This role is ideal for someone who enjoys working на стику data engineering, architecture, and cross-team collaboration, and хоче впливати на стратегічні рішення у data ecosystem.

🌍 About the Project

You will be part of a large-scale initiative aimed at transforming how data is ingested, processed, and consumed across the organization.

The key focus is on migrating and optimizing data flows, ensuring seamless integration with modern platforms such as Databricks Unity Catalog, and enabling more efficient and scalable data consumption.

🧩 Key Responsibilities

• Analyze existing data pipelines and codebase to identify data entry points
• Design and execute data source migration strategies (source-to-target mapping, adapters development)
• Redesign data consumption processes to support integration with Databricks Unity Catalog
• Collaborate closely with cross-functional teams (data foundation & product teams)
• Ensure smooth transition and alignment across systems, including permissions and dependencies
• Drive best practices in data engineering and contribute to architectural improvements

🛠️ Tech Stack

• Python / Spark / SQL
• Azure Data Factory
• Apache Airflow
• Databricks ecosystem (Unity Catalog)

Project description:
The goal of the project is to onboard the commercial data into EDF and deliver the reporting platform through PowerBI on top of Golden Layer built by GD (Medallion architecture).

Client description:
It is a global food and beverage leader with a product portfolio including 22 brands that generate more than $1 billion each in annual retail sales.

We're looking for a Data Engineer to:
Design and implement scalable data pipelines using Azure Data Factory and Databricks (PySpark).
Develop the Gold layer (semantic layer) for accurate business reporting.
Utilize Azure DevOps for CI/CD and implement observability to ensure data integrity.

Required Technology Stack:
Cloud: Azure
Processing: Databricks, PySpark
Storage: ADLS, Delta Lake
Orchestration: Azure Data Factory (ADF)
DevOps: Azure DevOps
Reporting: Power BI
Concepts: Medallion Architecture, Observability

Duration: 3–5 months
Location: Armenia, Ukraine, Poland, Estonia
 

Responsibilities:

• Shape and contribute to Cargoo’s data engineering strategy, including the design, architecture, and implementation of scalable data solutions
• Build, maintain, and evolve the Data Platform to support advanced analytics, machine learning, and real-time decision-making
• Collaborate closely with AI engineers, BI analysts, and business stakeholders to translate data needs into robust technical solutions
• Identify data opportunities across the organization and enable teams to extract maximum value from data assets
• Design and implement ETL/ELT pipelines, including batch and streaming data workflows
• Develop and maintain data models using dimensional and advanced modeling techniques
• Support deployment, monitoring, and lifecycle management of data applications
• Apply DataOps best practices to ensure reliability, scalability, and quality of data pipelines
• Participate in code reviews and promote high engineering standards
• Take ownership of projects end-to-end, ensuring timely delivery and measurable impact
   

Requirements:

• Solid proficiency in Python (runtime environment, package management) and SQL (DML, DDL)
• Hands-on experience with SQL Server / Azure SQL Server
• Experience working with cloud platforms, preferably Microsoft Azure
• Familiarity with the modern data stack, including tools such as dbt and orchestration frameworks (Airflow, Dagster, or similar)
• Strong understanding of ETL/ELT concepts, data architecture, and data modeling
• Experience with streaming technologies (Kafka or equivalent)
• Experience with Docker and container orchestration technologies
• Experience deploying and monitoring applications on Kubernetes (K8s)
• Knowledge of application lifecycle management
• Understanding and application of DataOps practices
• Strong project management, execution skills, and a clear sense of ownership and accountability

Project Overview

The company helps hotel chains like Hilton and Marriott transform front-desk staff into revenue generators through upselling training and analytics. Their IN-Gauge SaaS platform empowers frontline teams across 2,000+ properties in over 100 countries.

Responsibilities

- Implement scaling of the case detection process (scanner)

- Split API into multiple API services

- Emulate pubsub/cloud run locally

- Implement scaling of the case detection process (scanner)

- Split API into multiple API services

- Emulate pubsub/cloud run locally

- Extract & process data using PySpark + S3 (Kafka/Kinesis — bonus)

- Write & optimize SQL on PostgreSQL/Redshift; use DynamoDB as data source/sink

- Run and debug ETL jobs on Glue or EMR; monitor via console/Spark UI

Requirements (Must-have)

Data engineering - 5 years,

AWS - 3 years (1 year latest project),

Spark/PySpark - 2 years (1 year latest project)

Nice to Have

GCP (pubsub/Cloud Run)

Hiring Process

Intro call

Technical discussion (focused on real experience)

Offer

Start: ASAP

Description

Client is a luxury skincare and beauty brand. The brand is based in San Francisco and sells luxury skincare products worldwide.

Tatcha’s main IT “product” is its e-commerce website, which functions as a digital platform to sell products, educate customers, and personalize experiences.

• Runs on Salesforce Commerce Cloud (formerly Demandware) — an enterprise e-commerce platform that supports online shopping, order processing, customer accounts, and product catalogs.
• Hosted on cloud infrastructure (e.g., AWS, Cloudflare) for reliable performance and security
  Uses HTTPS/SSL encryption to secure data transfers.
• Integrated marketing and analytics technologies such as Klaviyo (email & SMS automation), Google Tag Manager, and personalization tools to track behavior, optimize campaigns, and increase conversions

It’s both a shopping platform and a digital touchpoint for customers worldwide.

Requirements

• 6+ years of experience as a Data Engineer, Analytics Engineer, or in a similar data-focused role.
• Strong SQL skills for complex data transformations and analytics-ready datasets.
• Hands-on experience with Python for data pipelines, automation, and data processing.
• Experience working with cloud-based data platforms (AWS preferred).
• Solid understanding of data warehousing concepts (fact/dimension modeling, star schemas).
• Experience building and maintaining ETL/ELT pipelines from multiple data sources.
• Familiarity with data quality, monitoring, and validation practices.
• Experience handling customer, transactional, and behavioral data in a digital or e-commerce environment.
• Ability to work with cross-functional stakeholders (Marketing, Product, Analytics, Engineering).

Nice to have:

• Experience with Snowflake, Redshift, or BigQuery.
• Experience with dbt or similar data transformation frameworks.
• Familiarity with Airflow or other orchestration tools.
• Experience with marketing and CRM data (e.g. Klaviyo, GA4, attribution tools).
• Exposure to A/B testing and experimentation data.
• Understanding of privacy and compliance (GDPR, CCPA).
• Experience in consumer, retail, or luxury brands.
• Knowledge of event tracking and analytics instrumentation.
• Ability to travel + visa to the USA
•  

Job responsibilities

• Design, build, and maintain scalable data pipelines ingesting data from multiple sources:
  e-commerce platform (e.g. Salesforce Commerce Cloud), CRM/marketing tools (Klaviyo), web analytics, fulfillment and logistics systems.
• Ensure reliable, near-real-time data ingestion for customer behavior, orders, inventory, and marketing performance.
• Develop and optimize ETL/ELT workflows using cloud-native tools.
• Model and maintain customer, order, product, and session-level datasets to support analytics and personalization use cases.
• Enable 360° customer view by unifying data from website interactions, email/SMS campaigns, purchases, and returns.
• Support data needs for personalization tools (e.g. product recommendation quizzes, ritual finders).
• Build datasets that power marketing attribution, funnel analysis, cohort analysis, and LTV calculations.
• Enable data access for growth, marketing, and CRM teams to optimize campaign targeting and personalization
• Ensure accurate tracking and validation of events, conversions, and user journeys across channels.
• Work closely with Product, E-commerce, Marketing, Operations, and Engineering teams to translate business needs into data solutions.
• Support experimentation initiatives (A/B testing, new digital experiences, virtual stores).
• Act as a data partner in decision-making for growth, CX, and operational efficiency.
• Build and manage data solutions on cloud infrastructure (e.g. AWS).
• Optimize storage and compute costs while maintaining performance and scalability.

What we offer

Culture of caring. At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people first. From day one, you’ll experience an inclusive culture of acceptance and belonging, where you’ll have the chance to build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders. 

Learning and development. We are committed to your continuous learning and development. You’ll learn and grow daily in an environment with many opportunities to try new things, sharpen your skills, and advance your career at GlobalLogic. With our Career Navigator tool as just one example, GlobalLogic offers a rich array of programs, training curricula, and hands-on opportunities to grow personally and professionally.

Interesting & meaningful work. GlobalLogic is known for engineering impact for and with clients around the world. As part of our team, you’ll have the chance to work on projects that matter. Each is a unique opportunity to engage your curiosity and creative problem-solving skills as you help clients reimagine what’s possible and bring new solutions to market. In the process, you’ll have the privilege of working on some of the most cutting-edge and impactful solutions shaping the world today.

Balance and flexibility. We believe in the importance of balance and flexibility. With many functional career areas, roles, and work arrangements, you can explore ways of achieving the perfect balance between your work and life. Your life extends beyond the office, and we always do our best to help you integrate and balance the best of work and life, having fun along the way!

High-trust organization. We are a high-trust organization where integrity is key. By joining GlobalLogic, you’re placing your trust in a safe, reliable, and ethical global company. Integrity and trust are a cornerstone of our value proposition to our employees and clients. You will find truthfulness, candor, and integrity in everything we do.

• We are seeking a skilled, hands-on Data Engineer with demonstrated experience in Databricks, Apache Spark, Python, and real-time data streaming solutions with experience in healthcare domain.

   

• Responsibilities:

  - Implement resource composition logic provided by the Product team into scalable and efficient PySpark code.
  - Develop, optimize, and maintain PySpark applications within the Databricks environment.
  - Utilize and maintain existing CI/CD pipelines in Databricks for automated build, testing, and deployment.
  - Write and maintain unit tests to ensure high code quality and reliability.
  - Perform data validation and quality checks to ensure compliance with defined business rules
  - Support and manage production deployments, ensuring system stability and performance.
  - Troubleshoot and resolve issues across development, testing, and production environments.
  - Collaborate with Product, QA, DevOps, and healthcare domain teams to ensure accurate implementation.
  - Maintain technical documentation for code, workflows, and deployment processes.
  - Pull from and push code to GitHub repositories, following version control and branching best practices.

   

• Mandatory Skills Description:

  - 5+ years of experience in data engineering roles
  - Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
  - 4+ years of experience with Databricks data pipelines
  - Strong hands-on experience with PySpark and/or Apache Spark.
  - Experience working with CI/CD pipelines and DevOps best practices.
  - Proficiency with CI/CD, GitHub (pull requests, branching, code reviews).
  - Experience writing unit tests and performing data validation
  - Experience supporting production systems and deployments
  - Strong analytical and troubleshooting skills.

   

• Nice-to-Have Skills Description:

  - Knowledge of FHIR (Fast Healthcare Interoperability Resources) standards.
  - Experience with healthcare data integration projects.
  - Familiarity with REST APIs and FHIR server integrations.
  - Experience in Agile/Scrum development environments.

   

• Languages:
  • English: C1 Advanced

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an ETL / Data Pipeline Specialist to join one of our clients' teams. If you're looking for an exciting opportunity to grow in a innovative environment, this could be the perfect fit for you.

Location: Remote
Reports To: Senior ETL Specialist
Employment Type: Full-Time Employee
Working Hours: European timezone with some overlap to USA
 

Role Overview
We are looking for an ETL / Data Pipeline Engineer to join our data infrastructure team. This role operates primarily within a European timezone, with some overlap with US Central Time to support collaboration with our Houston-based team. You will help manage and monitor our existing fleet of web scrapers, build new data collection pipelines for regulatory and energy market sources, and contribute to the broader evolution of our ETL architecture.

Key Responsibilities
Overnight Operations & Data Quality
• Monitor and manage overnight scraper and ingestion runs, triaging failures and applying fixes in real time to minimize data gaps before US market open
• Verify data completeness and quality across all automated feeds, flagging anomalies and coordinating with the Houston team on persistent issues
• Maintain run logs, error documentation, and escalation notes for seamless async handoffs
New & Expanded Data Collection
Build and maintain scrapers, parsers, and ingestion pipelines across a growing set of energy market data domains, including but not limited to:
• Pipeline operator portals, electronic bulletin boards, and related filings (notices, maintenance, capacity, gas quality, customer indices)
• Government and regulatory agency databases at the federal, state, and provincial level across North America
• International energy data sources covering European, Canadian, and Mexican supply, demand, and power markets
• Emissions and environmental reporting systems
• Financial and corporate filings, including public company disclosures and production reporting
• Geospatial and mapping data related to production, infrastructure, and market geography
ETL Architecture & Enhancement
• Contribute to the design and build-out of our broader ETL infrastructure, including scheduling, orchestration, and error handling
• Write transformation logic to clean, normalize, and load raw data into PostgreSQL staging and production tables
• Optimize existing pipelines for performance, reliability, and cost efficiency
• Help build monitoring dashboards and alerting for pipeline health and data freshness
• Document data lineage, schema changes, and pipeline dependencies
Technical Environment
• Languages: Python (primary), SQL, Bash scripting
• Database: PostgreSQL, Mongo and Snowflake
• Infrastructure: AWS (EC2, S3, Lambda), Docker
• Scraping: Selenium, Playwright, BeautifulSoup, Scrapy, or similar frameworks
• Orchestration: cron, Airflow, or equivalent scheduling tools
• Version Control: Git / GitHub
• Communication: Slack, with async handoffs to US-based team

Required Qualifications
• 3+ years of experience building and maintaining ETL pipelines or data engineering systems
• Strong Python skills with experience in web scraping, data parsing, and automation
• Proficiency in SQL and experience working with relational databases (PostgreSQL preferred)
• Experience with headless browsers, anti-bot mitigation, and scraping resilience patterns
• Strong debugging instincts and ability to triage pipeline failures quickly
• Clear written communication in English for async collaboration and documentation
Preferred Qualifications
• Experience with energy, commodities, or financial data pipelines
• Familiarity with FERC-regulated pipeline data, EIA reporting, or utility/regulatory filings
• Experience with PostgreSQL-specific features (partitioning, materialized views, JSONB, pg_cron, logical replication)
• Familiarity with infrastructure-as-code (Terraform, CloudFormation) or containerized deployments
• Prior experience working on a distributed team across time zones
What We Offer
• Competitive compensation and benefits with room to grow as the team scales
• Direct impact on the data infrastructure behind a leading natural gas intelligence platform
• A small, senior team where your work is visible and valued from day one
• Flexible remote work with clear async workflows
• Exposure to the North American energy markets and commodity data at scale

N-iX is looking a proactive Data Engineer to join our vibrant team.

As a Data Engineer, you will play a critical role in designing, developing, and maintaining sophisticated data pipelines, Ontology Objects, and Foundry Functions within Palantir Foundry. The ideal candidate will possess a robust background in cloud technologies, data architecture, and a passion for solving complex data challenges.

Key Responsibilities:

• Collaborate with cross-functional teams to understand data requirements, and design, implement and maintain scalable data pipelines in Palantir Foundry, ensuring end-to-end data integrity and optimizing workflows.
• Gather and translate data requirements into robust and efficient solutions, leveraging your expertise in cloud-based data engineering. Create data models, schemas, and flow diagrams to guide development.
• Develop, implement, optimize and maintain efficient and reliable data pipelines and ETL/ELT processes to collect, process, and integrate data to ensure timely and accurate data delivery to various business applications, while implementing data governance and security best practices to safeguard sensitive information.
• Monitor data pipeline performance, identify bottlenecks, and implement improvements to optimize data processing speed and reduce latency. 
• Troubleshoot and resolve issues related to data pipelines, ensuring continuous data availability and reliability to support data-driven decision-making processes.
• Stay current with emerging technologies and industry trends, incorporating innovative solutions into data engineering practices, and effectively document and communicate technical solutions and processes.

Tools and skills you will use in this role:

• Palantir Foundry
• Python
• PySpark
• SQL
• TypeScript

Requirements:

• 5+ years of experience in data engineering, preferably within the pharmaceutical or life sciences industry;
• Strong proficiency in Python and PySpark;
• Proficiency with big data technologies (e.g., Apache Hadoop, Spark, Kafka, BigQuery, etc.);
• Hands-on experience with cloud services (e.g., AWS Glue, Azure Data Factory, Google Cloud Dataflow);
• Expertise in data modeling, data warehousing, and ETL/ELT concepts;
• Hands-on experience with database systems (e.g., PostgreSQL, MySQL, NoSQL, etc.);
• Proficiency in containerization technologies (e.g., Docker, Kubernetes);
• Effective problem-solving and analytical skills, coupled with excellent communication and collaboration abilities;
• Strong communication and teamwork abilities;
• Understanding of data security and privacy best practices;
• Strong mathematical, statistical, and algorithmic skills.

Nice to have:

• Certification in Cloud platforms, or related areas;
• Experience with search engine Apache Lucene, Webservice Rest API;
• Familiarity with Veeva CRM, Reltio, SAP, and/or Palantir Foundry;
• Knowledge of pharmaceutical industry regulations, such as data privacy laws, is advantageous;
• Previous experience working with JavaScript and TypeScript.

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

Quantum is a global technology partner delivering high-end software products that address real-world problems. 

We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.

Here at Quantum, we are dedicated to creating state-of-the-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organizations globally attain technological leadership.

We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn’t yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!

About the position

Quantum is expanding the team and has brilliant opportunities for a proactive Senior Data Engineer who can design, implement, and evolve scalable data systems in AWS. We are building a greenfield analytics platform supporting both batch and real-time data processing. This role is a strategic blend of hands-on development, high-level architectural decision-making, and end-to-end platform ownership.

The client is a technological research company that utilizes proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in all languages. Our mission is to bridge the knowledge gap in the investment world and empower investors of all types to become “super-investors.”

Through our generative AI technology implemented into brokerage platforms and other financial institutions’ infrastructures, we offer instant fundamental analyses of global stocks alongside bespoke investment strategies, enabling informed investment decisions for millions of investors worldwide.

Must have skills:

• 5+ years of experience in Data Engineering
• Strong hands-on experience with Apache Spark (including Structured Streaming)
• Experience building both batch and streaming pipelines in production environments
• Proven experience designing AWS-based data lake architectures (S3, EMR, Glue, Athena)
• Experience with event streaming platforms such as Apache Kafka or Amazon Kinesis
• Experience implementing lakehouse formats such as Delta Lake
• Strong understanding of partitioning strategies and schema evolution
• Experience using SparkUI and AWS CloudWatch for profiling and optimization
• Strong understanding of Spark performance tuning (shuffle, skew, memory, partitioning)
• Proven track record of cost optimization in AWS environments
• Experience with Docker and CI/CD pipelines
• Experience with Infrastructure as Code (Terraform, AWS CDK, or similar)
• Familiarity with monitoring and observability practices
• Upper-Intermediate or higher English proficiency (spoken and written)

Would be a plus:

• Experience in the financial domain
• Experience running Spark workloads on Kubernetes
• Exposure to or interest in Large Language Models (LLMs) and AI integration
• Experience implementing data quality frameworks or metadata/lineage systems

Your tasks will include:

• Design and implement batch and streaming data pipelines using Apache Spark
• Build and evolve a scalable AWS-based data lake architecture
• Develop and maintain real-time data processing systems (event-driven pipelines)
• Own performance tuning and cost optimization of Spark workloads
• Define best practices for data modeling, partitioning, and schema evolution
• Implement monitoring, observability, and data quality controls
• Contribute to infrastructure automation and CI/CD for data workflows

• Participate in architectural decisions and mentor other engineers

   

We offer:

• Delivering high-end software projects that address real-world problems
• Surrounding experts who are ready to move forward professionally
• Professional growth plan and team leader support
• Taking ownership of R&D and socially significant projects
• Participation in worldwide tech conferences and competitions
• Taking part in regular educational activities
• Being a part of a multicultural company with a fun and lighthearted atmosphere
• Working from anywhere with flexible working hours

• Paid vacation and sick leave days

   

Join Quantum and take a step toward your data-driven future.

We are seeking a skilled, hands-on Data Engineer with demonstrated experience in Databricks, Apache Spark, Python, and real-time data streaming solutions.

Responsibilities

Implement resource composition logic provided by the Product team into scalable and efficient PySpark code.

Develop, optimize, and maintain PySpark applications within the Databricks environment.

Utilize and maintain existing CI/CD pipelines in Databricks for automated build, testing, and deployment.

Write and maintain unit tests to ensure high code quality and reliability.

Perform data validation and quality checks to ensure compliance with defined business rules

Support and manage production deployments, ensuring system stability and performance.

Troubleshoot and resolve issues across development, testing, and production environments.

Collaborate with Product, QA, DevOps, and healthcare domain teams to ensure accurate implementation.

Maintain technical documentation for code, workflows, and deployment processes.

Pull from and push code to GitHub repositories, following version control and branching best practices.

Skills

Must have

5+ years of experience in data engineering roles

Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).

4+ years of experience with Databricks data pipelines

Strong hands-on experience with PySpark and/or Apache Spark.

Experience working with CI/CD pipelines and DevOps best practices.

Proficiency with CI/CD, GitHub (pull requests, branching, code reviews).

Experience writing unit tests and performing data validation

Experience supporting production systems and deployments

Strong analytical and troubleshooting skills.

Nice to have

Knowledge of FHIR (Fast Healthcare Interoperability Resources) standards.

Experience with healthcare data integration projects.

Familiarity with REST APIs and FHIR server integrations.

Experience in Agile/Scrum development environments.