Data Engineer

Tasks/Responsibilities

• Platform ownership: Architect, build, and own production data pipelines and storage systems ingesting large-scale financial datasets (ICE, Bloomberg, LSEG, BlackRock, Clearwater, external managers etc).

• Data stewardship & governance: Take ownership of in-house managed data and embed robust data governance and control mechanisms across the pipeline lifecycle, and maintain comprehensive documentation.

• API & service design: Contribute to production APIs (FastAPI) powering internal applications as scalable and service-based architectures.

• Quality & reliability: Design and implement rigorous test plans, non-regression testing and data validation frameworks to guarantee the reliability and accuracy of pipelines that directly drive investment decisions.

• Engineering standards: Champion best practices for code quality, CI/CD (GitHub Actions) and observability/monitoring across the team's data and platform work.

• Team collaboration: Partner with quant researchers to translate portfolio analytics needs into robust application and with the engineers to build production-grade data architecture.

Required Skills / Experience

• 5+ years of professional experience in data engineering or a closely related discipline, including demonstrated ownership of production-grade data platforms (not solely research or prototype work).

• Strong proficiency in Python and SQL.

• Hands-on experience with cloud platforms.

• Experience designing and maintaining ETL/data pipeline architecture at scale, including multi-environment (DEV/STAGE/PROD) deployment practices.

• Experience with CI/CD pipelines (e.g., GitHub Actions) and observability/monitoring practices for production systems.

• Familiarity building or supporting backend APIs (FastAPI or equivalent).

• Experience with data quality assurance, including non-regression testing and data validation frameworks.

• Comfortable tracing data flow and troubleshooting issues under limited time restriction.

• Strong communicator and collaborator, with the ability to work independently, challenge requirements where needed and align technical solutions to real investment workflows.

Desired Skills / Experience

• Experience with financial, banking or asset-management companies.

• Experience with AI (RAG systems, document intelligence or agentic workflows).

• Hands-on experience with Snowflake and Databricks.

Education

• BS in Engineering, Computer Science, Information Technology, or equivalent practical experience