Engineering Manager, Data Engineering - Virta Health

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.

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.

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.

Responsibilities

• Lead and grow both engineering disciplines: Coach data engineers and analytics engineers, set clear expectations, and support their technical and career growth.
• 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.
• Own discovery, priorities, and delivery: Build relationships with Engineering, Analytics, Finance, Commercial, and Clinical partners to translate needs into a focused roadmap.
• Expand self-service: Build reusable ingestion and transformation patterns, tooling, documentation, and standards.
• Own analytics engineering quality: Guide the development of reusable dbt models, shared semantic definitions, and LookML.
• 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.
• Improve reliability and governance: Establish clear ownership, testing, freshness expectations, actionable alerts, and incident practices.
• Improve how the team works: Apply AI-assisted development and automation where they reduce toil and improve delivery.

Requirements

• 3+ years of engineering management experience, including direct leadership of both data engineering and analytics engineering work.
• Strong technical judgment across data pipelines, cloud warehouses, SQL and data modeling, transformation frameworks, orchestration, and BI or semantic layers.
• Experience leading a significant data platform or analytics migration.
• Experience owning stakeholder discovery and roadmap prioritization without relying on a dedicated product manager.
• A track record of building reusable capabilities and helping other teams adopt them.
• Experience hiring, coaching, and managing performance in a remote or asynchronous environment.
• Clear communication with technical and business audiences.

Bonus Points For

• Experience with BigQuery, dbt Cloud, Looker/LookML, GCP, Python, Go, or infrastructure as code.
• Experience building data agents, governed natural-language analytics, semantic layers for AI, or data capabilities that support production ML systems.
• Experience introducing AI-assisted development workflows with effective testing and review practices.
• Experience in healthcare or another regulated industry, including sensitive-data governance and access controls.