About the job
About Judi Health
Judi Health is a pioneering enterprise health technology company dedicated to transforming healthcare with a robust suite of solutions tailored for employers and health plans, which encompass:
- Capital Rx, a public benefit corporation providing comprehensive pharmacy benefit management (PBM) solutions for self-insured employers,
- Judi Health™, delivering end-to-end health benefit management solutions for employers, TPAs, and health plans, and
- Judi®, the premier proprietary Enterprise Health Platform (EHP) that integrates all claim administration workflows into a single, scalable, and secure platform.
In collaboration with our clients, we are committed to restoring trust in the U.S. healthcare system and establishing the necessary infrastructure for optimal care. To explore more about our initiatives, visit www.judi.health.
Position Summary:
We are on the lookout for a driven and skilled Data Engineer I (Analytics Engineering) to contribute to the growth and enhancement of Capital Rx’s analytics infrastructure. This role is pivotal in creating high-quality, analytics-ready datasets and a unified metrics/semantic layer that fuel reporting, self-service business intelligence, and operational insights. You will extensively utilize dbt and Snowflake to architect robust data models, define reliable business metrics, and uphold data quality through thorough testing, documentation, and observability—working in close partnership with operational, product, and analytics teams to convert business inquiries into reusable data products that yield measurable results.
Position Responsibilities:
· Collaborate with operational and analytics teams to convert business requirements into analytics-ready data models, curated data marts, and reusable datasets.
· Develop and manage dbt projects (staging → intermediate → marts), including model layering, macros, exposures, packages, and source freshness to expedite delivery and enhance reliability.
· Design and refine dimensional models (facts/dimensions, grain, SCD patterns) and analytics patterns aligned with key business domains.
· Take ownership of metrics definitions from start to finish: establish KPI logic, naming conventions, calculation guidelines, and governance; maintain a metrics/semantic layer to ensure stakeholders utilize consistent definitions across tools and teams.
· Implement stringent data quality protocols (dbt tests, constraints, anomaly checks/monitoring) to guarantee dependable downstream reporting and decision-making.
· Create clear documentation and enablement materials (dbt docs, lineage, data dictionaries, metric definitions, onboarding guides, etc.) to facilitate user understanding and engagement.

