Grid Dynamics

Data engineer in payments

In short

Data Engineer role building a new data platform on Databricks/AWS for a global payments technology company, working on a lean, engineering-focused team alongside a Tech Lead and client engineers. Scope includes Spark/PySpark pipelines across a medallion architecture, ClickHouse structures for real-time analytics, and support for a legacy Hadoop platform. Requires commercial data engineering experience, hands-on Databricks and Spark skills, strong Python/SQL, and ownership of automated testing and data quality since there is no dedicated QA. Onsite, full-time role in Warsaw.

Описание
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.
Задачи
•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.
•Develop and optimize Spark / PySpark workloads, including those behind monthly reports for around 40,000 merchants.
•Build ClickHouse ingestion and analytical structures for low-latency use cases.
•Implement automated tests, integration tests and data quality checks yourself, with no dedicated QA on the team, and take part in code reviews.
•Troubleshoot production issues and deliver targeted fixes in a legacy Hadoop platform with file-based sources and Kafka streams (around 10% of your time).
•Contribute to shared engineering patterns that become the standard for the new platform, with potential growth into AI/ML and MLOps.
Требования
•Commercial experience in data engineering, building production data pipelines and transformations.
•Hands-on experience building pipelines and workloads on Databricks.
•Strong practical skills in developing and optimizing Spark workloads.
•Strong programming skills in Python and SQL.
•Ability to own automated testing and data quality validation as part of everyday delivery.
Будет плюсом
•Strong experience building automated testing for data platforms
•Knowledge of the Hadoop ecosystem, Kafka and streaming data
•Experience with ClickHouse for real-time or low-latency analytics
•Fundamentals of AI/ML and MLOps, including Databricks MLOps
•Experience with AWS, Delta Lake, CI/CD and performance optimization
Условия
•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

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