• Build the Semantic Layer: Design and implement production-grade dbt models that transform raw source data into clean, documented, and tested Data Marts. • Enforce Data Quality: Move beyond basic NOT NULL checks. Implement semantic validation rules (using dbt tests or Great Expectations) that catch business logic failures before they hit the dashboard. • Own the Pipeline: Manage the lifecycle of your data models from IDE to production, including code review, CI/CD integration, and performance tuning in Snowflake. • Bridge the Gap: Collaborate with Platform Engineers to understand upstream ingestion patterns (Python/Fivetran) and debug data issues at the source. • Design the core Star Schemas and data models that define our business. • Act as the primary interface between Clinical Operations and Engineering. You will interview stakeholders to uncover the "Why" behind their requests and translate vague business needs into precise technical specifications. • Mentorship & Standards: Set the standard for SQL style, dbt macro usage, and testing rigor. Mentor intermediate engineers on healthcare domain nuances (e.g., why a reversal claim behaves differently than a void).