• Apply hands-on analytics engineering expertise to solve complex, fast-paced business and clinical problems using data. • Design, develop and maintain scalable, analytics ready data models and pipelines (primarily in dbt) that power clinical program reporting, metrics and insights in our Unified Data Warehouse (UDW). • Partner closely with clinical program leaders, analysts, product teams, and operations stakeholders to understand program workflows (e.g., enrollment, adherence, outcomes, follow ups) and translate them into intuitive, well-documented data models. • Build curated data marts and semantic layers that make analytics for clinical programs (such as diabetes and weight management) easy to self-serve for dashboards, ad-hoc analysis and operational reporting. • Own KPI and metric definitions end-to-end, from raw source data through production-grade models, ensuring consistency, transparency and trust in reporting. • Implement automated data validation, testing and quality checks to ensure high data reliability across clinical and operational datasets. • Continuously optimize data models and warehouse performance for both cost and speed as usage scales. • Contribute to analytics governance by establishing guardrails, documentation and best practices that improve accountability, ownership, and data literacy across teams. • Communicate insights effectively by developing clear data memos, documentation and presentations tailored to clinical and business stakeholders. • Explore and integrate AI-enabled analytics and predictive modeling use cases (e.g., adherence risk, program outcomes, operational forecasting) into the analytics stack. Experience operationalizing ML models is a plus.