Data Engineer

Grid Dynamics; Poznań; On-site; PLN 180,000 - 280,000; Full time

Benefits offered by this job

Bleeding-edge projects

Motivated team

Competitive salary

Flexible schedule

Medical insurance

Sports

Development opportunities

Well-equipped office

Job summary

Grid Dynamics is seeking a Data Engineer (Databricks / Spark) to join a lean, engineering-focused team building a new data platform for a global payments technology company. You will collaborate with the Tech Lead and client engineers to deploy Databricks on AWS pipelines and own automated tests and data quality checks.

You will also develop real-time analytics with ClickHouse, contribute to platform patterns, and support a legacy Hadoop setup, with opportunities to grow into AI/ML and MLOps.

Qualifications

• Commercial experience in data engineering, building production data pipelines and transformations.
• Databricks: Hands-on experience building pipelines and workloads on Databricks.
• Spark / PySpark: Strong practical skills in developing and optimizing Spark workloads.
• Python & SQL: Strong programming skills in Python and SQL.
• Quality Mindset: Ability to own automated testing and data quality validation as part of everyday delivery.

Responsibilities

• New Platform Development: Build pipelines and data transformations on Databricks on AWS, developing Bronze, Silver and Gold layers as part of a platform that unifies the engineering approach across many consumers and use cases.
• Spark Performance: Develop and optimize Spark / PySpark workloads, including those behind monthly reports for around 40,000 merchants.
• Real-Time Analytics: Build ClickHouse ingestion and analytical structures for low-latency use cases.
• Engineering Quality Ownership: Implement automated tests, integration tests and data quality checks yourself, with no dedicated QA on the team, and take part in code reviews.
• Legacy Platform Support: Troubleshoot production issues and deliver targeted fixes in a legacy Hadoop platform with file-based sources and Kafka streams (around 10% of your time).
• Reusable Patterns: Contribute to shared engineering patterns that become the standard for the new platform, with potential growth into AI/ML and MLOps.

Skills

Data engineering experience

Databricks

Spark PySpark

Python & SQL

Quality mindset

Job description

We are seeking a Data Engineer (Databricks / Spark) to join a lean, engineering-focused team building a new data platform for a global payments technology company. In this role, you will work closely with our Tech Lead and the client’s engineers to build Databricks on AWS pipelines across a medallion architecture, develop ClickHouse structures for real-time analytics, and own the quality of your code end to end, from automated tests to data quality checks, while giving pragmatic support to a legacy Hadoop platform.

Responsibilities

• New Platform Development: Build pipelines and data transformations on Databricks on AWS, developing Bronze, Silver and Gold layers as part of a platform that unifies the engineering approach across many consumers and use cases.
• Spark Performance: Develop and optimize Spark / PySpark workloads, including those behind monthly reports for around 40,000 merchants.
• Real-Time Analytics: Build ClickHouse ingestion and analytical structures for low-latency use cases.
• Engineering Quality Ownership: Implement automated tests, integration tests and data quality checks yourself, with no dedicated QA on the team, and take part in code reviews.
• Legacy Platform Support: Troubleshoot production issues and deliver targeted fixes in a legacy Hadoop platform with file-based sources and Kafka streams (around 10% of your time).
• Reusable Patterns: Contribute to shared engineering patterns that become the standard for the new platform, with potential growth into AI/ML and MLOps.

Requirements

• Commercial experience in data engineering, building production data pipelines and transformations.
• Databricks: Hands-on experience building pipelines and workloads on Databricks.
• Spark / PySpark: Strong practical skills in developing and optimizing Spark workloads.
• Python & SQL: Strong programming skills in Python and SQL.
• Quality Mindset: Ability to own automated testing and data quality validation as part of everyday delivery.

Nice to have

• Data Platform Test Automation: Strong experience building automated testing for data platforms.
• Hadoop & Streaming: Knowledge of the Hadoop ecosystem, Kafka and streaming data.
• ClickHouse: Experience with ClickHouse for real-time or low-latency analytics.
• AI/ML & MLOps: Fundamentals of AI/ML and MLOps, including Databricks MLOps.
• Cloud & Delivery: Experience with AWS, Delta Lake, CI/CD and performance optimization.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About Us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.