Job description
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.
Skills
- 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
- 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.
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