We are seeking a highly motivated and talented Data Engineer I (Analytics Engineering) to help build and scale Capital Rx’s analytics foundation. This role focuses on developing high-quality, analytics-ready datasets and a consistent metrics/semantic layer that power reporting, self-service BI, and operational insights. Partner closely with operational and analytics teams to translate business needs into analytics-ready data models, curated data marts, and reusable datasets. Build and maintain dbt projects (staging → intermediate → marts), including model layering, macros, exposures, packages, and source freshness to accelerate delivery and improve reliability. Design and evolve dimensional models (facts/dimensions, grain, SCD patterns) and analytics patterns aligned with key business domains. Own metrics definitions end-to-end: establish KPI logic, naming standards, calculation rules, and governance; maintain a metrics/semantic layer so stakeholders use consistent definitions across tools and teams. Implement robust data quality practices (dbt tests, constraints, anomaly checks/monitoring) to ensure reliable downstream reporting and decision-making. Produce clear documentation and enablement assets (dbt docs, lineage, data dictionaries, metric definitions, onboarding guides) to support self-service analytics. Optimize warehouse performance and cost in Snowflake through SQL tuning, incremental models, appropriate materializations, and efficient data design. Use disciplined software engineering practices (Git-based workflows, peer review, CI checks, versioning, environment promotion) to deploy changes safely and repeatably. Proactively identify and resolve data-related issues, ensuring system reliability and data integrity. Provide ongoing support for critical analytics data products and participate in an on-call rotation as needed. Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance.