Full Job Description
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.
The base compensation range for this full-time position is between $135,000 - $155,000 plus benefits. Compensation decisions are supported through market data, where regional variances may exist based on cost of labor. We also take into consideration prior experience, relevant skills, education and/or training, certifications and, as applicable, other required qualifications. If you have questions regarding compensation, the talent acquisition team can provide relevant details during the interview process.
