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
Must have
- 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.