data engineer for investment teams

The team designs, builds, and operates data pipelines and models that power analytics and applications across investment teams and Middle/Back office. It works with financial market data, including securities, prices, corporate actions, and positions/holdings.

• Clarify requirements and participate in sprint planning sessions;
• Design and implement technical solutions, including ETL pipelines in PySpark;
• Optimize existing ETL processes for performance, scalability, and reliability;
• Write unit and integration tests;
• Support QA teammates during acceptance;
• Resolve production incidents as a third-line engineer;
• Own data end-to-end, from ingesting vendor and internal sources to modeling data in the lakehouse and making it discoverable, reliable, and cost-efficient;
• Partner with BAs/PMs and quants, anticipate downstream needs, and propose pragmatic architectures that balance speed, governance, and scalability.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline;
• 5+ Years of experience in data engineering roles; flexibility depends on depth of capability;
• Strong hands-on experience with Databricks in production environments;
• Strong programming experience with PySpark and strong SQL skills;
• Proven experience with Declarative Pipelines or pipeline orchestration on Databricks;
• Strong understanding of data engineering fundamentals, including ingestion patterns, transformation design, incremental processing, testing, and performance tuning;
• Experience delivering production-ready datasets with operational controls for monitoring, troubleshooting, and reliability;
• Experience with modern Lakehouse concepts, including Delta tables, optimization strategies, file skipping, and metadata/statistics awareness;
• Exposure to data governance practices, including cataloguing, documentation, business glossary/terms, and lineage;
• Experience in enterprise environments with CI/CD pipelines and structured release processes;
• Familiarity with vendor market data feeds or similar multi-source mastering patterns;
• English: C1 Advanced;
• Nice to have: hands-on expertise in Palantir Foundry, experience with Foundry pipelines, ontologies, data lineage, transformations, and platform governance, experience leading or executing migrations from Palantir to Databricks, including pipeline conversion, data model redesign, and production cutover, familiarity with Dynatrace or Datadog, Databricks, cloud (Azure/AWS), or enterprise data architecture certifications.

• Relocation options are available, though relocation is not available for all open positions;
• At Luxoft Poland, remote work is available only from within Poland;
• Annual holiday: 20 or 26 days, depending on overall seniority;
• Occasional leave: 1 or 2 days, depending on circumstances;
• Child care leave: 2 days or 16 hours per year;
• Absence due to force majeure: 2 days or 16 hours per year;
• Maternity leave: 20 weeks; parental leave: 41 weeks; paternity leave: 14 days;
• Benefits depend on the form of cooperation and apply to employees under a contract of employment;
• Benefits include expert-led technical courses, soft skills courses, self-learning resources and libraries, cloud and custom learning programs, technical mentorship, leadership programs for managers, a Multisport card, wellbeing activities, preferential banking and car leasing offers, cafeteria discounts, social benefit fund support, private healthcare and dental care, travel and life insurance, corrective glasses reimbursement, social events, internal mobility, and a self-learning library.