Accelerate your career by working with the largest technology clients in the world. Join this diverse team that thrives on innovation and making a real-world impact.
This role sits at the intersection of platform engineering, data engineering, and data product development within financial services applications. Leading with platform engineering - and with data engineering as the assumed foundation - the engineer builds reusable platform capabilities and data products that let quantitative researchers discover, access, and consume data across a complex, disparate landscape, and then accelerates the good prototypes to production.
• Design, build, and enhance platform capabilities within Databricks and related technologies.
• Evaluate and adopt emerging platform features and technologies; run the self-assessments and technology evaluations research initiatives depend on.
• Improve governance, automation, observability, security, and operational excellence.
• Help define the target-state architecture for custom platform.
• Work through AI agents by default. Use agentic tools to design, build, test, and operate, and know when to trust them versus verify.
Required Skills
• Hands-on Databricks experience across workspace, notebooks, and jobs, plus building platform capabilities on it (not just using it).
• Strong Python and SQL.
• Platform-engineering foundation: infrastructure as code with Terraform and modern CI/CD.
• Data governance and observability in a managed environment (e.g. Unity Catalog, lineage, monitoring).
• Strong workflow orchestration experience with Apache Airflow (we use Astro / Astronomer).
• Building and operating data pipelines that integrate disparate, heterogeneous data sources.
• Building reusable, self-service data products for non-engineer end users (researchers/analysts).
• An AI-first mindset: fluent with agentic coding tools (e.g. Claude Code) and eager to make them central to how the team works.
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