Senior Data Engineer Investment Data

🔥Become a Luxoft employee🔥 Our Benefits: 💰Paid Referrals 💻Equipment: laptop and monitor 🩺Private Medical & Dental care & Life Insurance covered 🏋🏽 ♀️ MyBenefit program (sports card, well-being program etc.) 🌎 Internal Mobility program - possibility of rotation between projects, locations, accounts 🎓 LuxTalent platform (webinars, training, courses) ...and more! Project Description: We’re looking for an experienced, hands-on Data Engineer who is capable of designing, building, and operating data pipelines and models that power analytics and applications across investment teams and Middle/Back office. The ideal candidate has financial markets familiarity (securities, prices, corporate actions, positions/holdings) and thrives in ambiguous environments—proactively shaping solutions, not waiting for tickets. You’ll own data end-to-end: from ingesting vendor and internal sources, to modeling in our lakehouse, to making data discoverable, reliable, and cost-efficient. You’ll partner closely with BAs/PMs and quants, anticipate downstream needs, and propose pragmatic architectures that balance speed, governance, and scalability. Responsibilities: • Participate in requirements clarification and sprint planning sessions. • Design technical solutions and implement them, inc ETL Pipelines – Build robust data pipelines in PySpark to extract, transform, using PySpark • Optimize ETL Processes – Enhance and tune existing ETL processes for better performance, scalability, and reliability • Writing unit and integration tests. • Support QA teammates in the acceptance process. • Resolving PROD incidents as a 3rd line engineer. Mandatory Skills Description: • Bachelor’s degree (Computer Science, Engineering, Information Systems, or related discipline). • 5+ years experience in data engineering roles (flexible based on depth of capability). • Strong hands-on experience with Databricks in production environments (prerequisite). • Strong programming experience with PySpark (must) and strong SQL (must). • Proven experience with Declarative Pipelines / pipeline orchestration on Databricks (prerequisite). • Strong understanding of data engineering fundamentals: ingestion patterns, transformation design, incremental processing, testing, performance tuning. • Experience delivering production-ready datasets with appropriate operational controls (monitoring, troubleshooting, reliability patterns). • Experience with modern Lakehouse concepts (Delta tables, optimization strategies, file skipping, metadata/statistics awareness). • Exposure to data governance practices: cataloguing, documentation, business glossary/terms, lineage. • Experience working in enterprise environments with CI/CD pipelines and structured release processes. • Familiarity with vendor market data feeds (e.g., Bloomberg, Refinitiv, MSCI, FactSet) or similar multi-source mastering patterns. Nice-to-Have Skills Description: • Strong Hands-on Expertise in Palantir Foundry. Proven experience with Foundry pipelines, ontologies, data lineage, transformations, and platform governance. • Proven Migration Experience from Palantir / to Databricks. Demonstrated experience leading or executing platform migrations, including pipeline conversion, data model redesign, and production cutover. • Familiarity with Dynatrace or Datadog for system observability and monitoring. • Databricks certification, cloud certifications (Azure/AWS), or enterprise data architecture certifications. Languages: English: B2 Upper Intermediate