Data Engineer - Senior
Summary
Senior hands-on data engineer owning and scaling the data platform behind systematic credit strategies at Arch Investment Management: building production pipelines for large-scale financial datasets, contributing to FastAPI internal services, and championing CI/CD, testing and observability. Core stack: Python, SQL, cloud platforms, GitHub Actions; Snowflake/Databricks desired.
With a company culture rooted in collaboration, expertise and innovation, we aim to promote progress and inspire our clients, employees, investors and communities to achieve their greatest potential. Our work is the catalyst that helps others achieve their goals. In short, We Enable Possibility. Arch Investment Management Ltd. (AIML), based in Bermuda, is a wholly owned subsidiary of Arch Capital Group Ltd., a leading insurance and reinsurance company. Arch Capital has investable assets of over $50bn. Arch Investment Management manages Arch Capital and its subsidiaries' portfolios.
The Risk & Quantitative Strategies (RQS) team, responsible for producing the analytics and models that support business decisions, is growing and is hiring a senior data engineer to help own and scale the data platform underpinning our systematic credit strategies. This is a senior, hands-on role: you will be trusted to architect solutions independently, ingest data reliably from different sources, work directly with quant researchers to translate needs into production data systems, and raise the technical bar across the team.
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.