About the Role
This role sits within the data engineering function of a global investment manager, building the pipelines and infrastructure that feed portfolio management, research, and risk systems firmwide. Increasingly, that means building for AI and ML workloads directly, not just traditional reporting. When data pipelines break or data quality slips, downstream investment and research teams feel it immediately.
What You'll Do
- Design, build, and maintain scalable data pipelines supporting investment, research, and risk functions
- Build infrastructure to support AI and ML workloads, including feature pipelines and model-ready datasets
- Partner with quants, researchers, and data scientists to understand data requirements for AI initiatives
- Own data quality, monitoring, and reliability across critical pipelines
- Evaluate and integrate new data sources and tooling as the firm's AI use cases expand
Must-haves
- 3+ years of data engineering experience
- Strong SQL and Python skills
- Experience building or supporting pipelines that feed AI/ML models or applications
- Experience with cloud data platforms (AWS, Azure, GCP, or Snowflake/Databricks)
- Comfort working in a regulated, data-sensitive financial services environment
Nice-to-haves
- Experience with orchestration tools (Airflow, Dagster, or similar)
- Exposure to vector databases or LLM-adjacent data infrastructure
- Background working with investment, portfolio, or risk data specifically
- Experience at an asset manager, hedge fund, or similar buy-side firm
Why This Role
This role sits at the center of the firm's push to bring AI into investment and research workflows, with real budget and real priority behind it. Compensation is top of market. For a data engineer who wants their pipelines to directly power the firm's next generation of AI tools rather than just keep the lights on, this is that.
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