• Partner with Product Managers, Care Team leads, and Area owners to translate business goals into measurable hypotheses and actionable insights that drive product enhancements and member outcomes • Design and evaluate experiments (A/B tests and beyond) to validate product hypotheses and guide iteration • Partner with Product and ML teams to measure the impact of new features, workflows, and personalization systems • Build and maintain robust dbt models, dashboards, and self-serve analytics tools using Snowflake, dbt, and BI platforms • Define, monitor, and communicate KPIs across product areas — member engagement, CGM utilization, Care Team effectiveness, and more • Collaborate with Data Engineers to ensure the analytical layer is built on a reliable, well-structured data foundation • Build data pipelines for analytics and insight generation using Python, dbt, and Snowflake • Develop and implement data validation and monitoring strategies to ensure ongoing data integrity • Communicate findings clearly and effectively to both technical and non-technical stakeholders • Create and maintain technical documentation including data models, metric definitions, and data dictionaries • Contribute to the continuous improvement of data analytics processes and best practices • Other duties as assigned

• Develop and maintain data pipelines for ingestion, transformation, and provisioning of information; • Design and optimize data engineering processes to ensure data quality, reliability, and performance; • Build data models for analytics and decision-making; • Develop queries and advanced analyses to generate business insights; • Work on integrating data from multiple sources and systems; • Support the development and maintenance of dashboards and strategic KPIs; • Ensure data governance, traceability, and quality; • Collaborate with business, analytics, and technology teams to address analytical needs.

• Own end-to-end development of production-grade data models and pipelines • Build data quality checks, data contracts, validation logic, and monitoring • Partner with upstream engineering teams to fix data gaps • Support live regulatory exams, audits, and ad hoc regulator requests • Automate recurring manual workflows into scalable pipelines and self-serve tooling

• Develop and maintain data ingestion and transformation pipelines; • Work with Lakehouse architectures; • Work in GCP and Databricks environments; • Create and evolve data structures in BigQuery and Delta Lake; • Support the definition and application of dimensional and relational modeling (Star Schema, Snowflake, etc.); • Ensure data quality, consistency, and security; • Support performance and cloud cost monitoring activities; • Collaborate with business units and data teams.