Engineering Manager, Data Engineering

Virta Health is on a mission to reverse metabolic disease in one billion people. Current treatment approaches aren’t working—over half of US adults have either type 2 diabetes or prediabetes, and obesity rates are at an all-time high. Virta is changing this by helping people reverse their metabolic condition through innovations in technology, personalized nutrition, and virtual care delivery reinvented from the ground up. We have raised over $350 million from top-tier investors, and partner with the largest health plans, employers, and government organizations to help their employees and members restore their health and take back their lives. Join us on our mission to reverse metabolic disease in one billion people. ABOUT THE ROLE As Engineering Manager for Data Engineering, you will lead and grow the team responsible for the data foundations that support Virta’s business, care delivery, and AI capabilities. You will take over an established team with deep technical and business knowledge and help shape its next stage of growth. The team is composed of both Data Engineers (platform) and Analytics Engineers (models). You will be accountable for reliable pipelines, platform tooling, and engineering standards, as well as reusable analytical models, semantic definitions, and the Looker experiences that help people make decisions. Both disciplines are core to the role, with distinct skills and career paths to develop. Your initial priorities will be completing our data and analytics modernization, expanding self-service, and enabling agents and ML to use trusted data. Our stack includes BigQuery, dbt Cloud, and Looker/LookML. You will work directly with stakeholders to understand their needs, set priorities, and measure adoption. Strong engineers will be your partners in delivery; you should be comfortable getting into the technical details when decisions or problems require it. RESPONSIBILITIES: - Lead and grow both engineering disciplines. Coach data engineers and analytics engineers, set clear expectations, and support their technical and career growth. Assess the team’s needs and use additional planned hires to build the right mix of capabilities. - Complete the data and analytics migration. Lead the remaining BigQuery medallion architecture and Looker Core rollout, including validation, stakeholder onboarding, and retirement of legacy paths. Preserve confidence in business metrics throughout the transition. - Own discovery, priorities, and delivery. Build relationships with Engineering, Analytics, Finance, Commercial, and Clinical partners. Translate their needs into a focused roadmap, resolve competing priorities, and balance planned work with operational support. - Expand self-service. Build reusable ingestion and transformation patterns, tooling, documentation, and standards that help engineering and analytics teams produce and use trustworthy data with less central coordination. - Own analytics engineering quality. Guide the development of reusable dbt models, shared semantic definitions, and LookML. Partner with domain experts to establish consistent business meaning and create datasets that serve multiple use cases. - Advance data capabilities for agents and ML. Build and operate data agents, and provide governed datasets, semantic context, and tools for agents built by other teams. Partner with Applied AI and MLOps on the data access, quality, and interfaces needed for their ML workflows. - Improve reliability and governance. Establish clear ownership, testing, freshness expectations, actionable alerts, and incident practices. Partner with Security and Infrastructure on access controls, PHI/PII protection, lineage, and cost management. - Improve how the team works. Apply AI-assisted development and