Senior Associate of Morgan Health, Analytics Engineering

In 2021, JPMorgan Chase launched Morgan Health, a new business unit focused on improving the quality, opportunity, and affordability of employer-sponsored health care in the United States. Morgan Health pursues this strategy through investments, collaborations with the JPMC Benefits team, engagements with other market leaders, sophisticated data analytics and research, and policy advocacy. Morgan Health is headquartered in Washington, DC, with members of the team based in New York City and Boston. To learn more about our strategy and latest developments, please visit: www.morganhealth.com . As a Senior Associate of Analytics Engineering within Morgan Health, you will utilize your deep experience with SQL-based data modeling and the curated analytics/semantic layer, transformations, and analytics engineering best practices to build and scale a trusted analytics layer that powers Morgan Health’s data science insights. In this role, you will design and maintain curated data marts and measures for use by data scientists and analytical stakeholders. You will lean into your skills as a motivated problem-solver, team collaborator, and clear communicator to play a crucial role in progressing Morgan Health’s mission to improve health care for JPMorgan Chase health plan members and across employer-sponsored insurance. This role is in-office five days per week, based in either New York, NY or Washington, DC.

Build and update Morgan Health’s healthcare data model (e.g., marts and curated datasets) using SQL and dbt. Create proof of concept (POC) solutions of data products per data scientist design requirements; iterate rapidly to refine requirements and evolve POCs to production-level solutions. Develop and maintain robust automated testing (e.g., dbt tests and custom checks) for new and refreshed datasets. Perform code reviews and quality checks of peer work; adhere to coding standards and best practices; incorporate feedback to continuously improve maintainability and reliability. Maintain clear documentation (data dictionary, model descriptions, metric logic, known limitations, and issue logs) to promote transparency and support data governance and HIPAA-aligned compliance standards. Proactively investigate, identify root causes, and address data quality issues in the analytics layer; partner with the Data Product lead to validate upstream issues and prioritize solutions. Contribute to continuous improvement of analytics engineering practices, including CI/CD, development workflows, performance optimization. Required Qualifications, Capabilities, and Skills: Bachelor’s degree in a quantitative field such as computer science, information systems, engineering, or a related discipline 5 years of professional experience in analytics engineering, data analytics/modeling, or a closely related role with a focus on building curated, analyst- and data-science-facing datasets Strong SQL skills and hands-on experience building modular, tested, version-controlled SQL transformations using dbt or other transformation or semantic-layer tools (e.g., SQLMesh, Dataform, Coalesce, Matillion) Demonstrated ownership of the full lifecycle of a dbt (or comparable) project, including automated testing, CI/CD and deployment, environment/version management, monitoring, and ongoing performance and reliability of the analytics layer in production Experience working with Python or R for analysis, testing, or automation Proven track record of creating and maintaining data models, data marts, and transformations that support multiple competing analytical use cases Demonstrated ownership of the full lifecycle of a dbt (or comparable) project, including workflow design, automated testing, CI/CD and deployment, environment/version management, monitoring, and ongoing performance and reliability of the analytics layer in production Motivated to develop clear technical documentation (data dictionaries, lineage notes, model descriptions)