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Salary: USD 175,000 - 228,000 / annual
Virta Health is reversing metabolic disease through innovations in technology, personalized nutrition, and virtual care delivery. The company has raised over $350 million and partners with major health plans, employers, and government organizations.
As Engineering Manager for Data Engineering, you will lead and grow the team responsible for data foundations supporting Virta's business, care delivery, and AI capabilities. You will oversee an established team of both Data Engineers (platform) and Analytics Engineers (models), accountable for reliable pipelines, platform tooling, engineering standards, reusable analytical models, semantic definitions, and Looker experiences that drive decision-making.
Key responsibilities include:
- Leading and coaching both data engineering and analytics engineering disciplines, setting clear expectations and supporting technical and career growth
- Completing the data and analytics modernization, including BigQuery medallion architecture and Looker Core rollout with validation and stakeholder onboarding
- Owning discovery, priorities, and delivery by building relationships with Engineering, Analytics, Finance, Commercial, and Clinical partners
- Expanding self-service capabilities through reusable ingestion and transformation patterns, tooling, documentation, and standards
- Guiding analytics engineering quality through dbt models, shared semantic definitions, and LookML development
- Advancing data capabilities for AI agents and ML systems, providing governed datasets and semantic context
- Improving reliability and governance through clear ownership, testing, freshness expectations, and incident practices
- Leveraging AI-assisted development and automation to reduce toil while protecting data correctness
The tech stack includes BigQuery, dbt Cloud, Looker/LookML, and GCP. You will work directly with stakeholders to understand needs, set priorities, and measure adoption. Your initial priorities are completing the data and analytics modernization, expanding self-service, and enabling agents and ML to use trusted data.
The 90-day plan includes building relationships with the team and stakeholders, assessing capability gaps, aligning on migration milestones, establishing success measures, and delivering meaningful improvements with measurable user adoption.
REQUIREMENTS:
- 3+ years of engineering management experience with direct leadership of both data engineering and analytics engineering work (deep expertise in one discipline sufficient if you have successfully led engineers in the other)
- Strong technical judgment across data pipelines, cloud warehouses, SQL and data modeling, transformation frameworks, orchestration, and BI or semantic layers; ability to review designs, investigate problems, and make sound tradeoffs
- Experience leading a significant data platform or analytics migration, including data validation, stakeholder adoption, and operational continuity
- Experience owning stakeholder discovery and roadmap prioritization without a dedicated product manager; ability to understand business problems, challenge assumptions, and turn competing requests into achievable plans
- Track record of building reusable capabilities and helping other teams adopt them; balance shared standards with domain-specific needs
- Experience hiring, coaching, and managing performance across different engineering skill sets; building collaborative teams in remote or asynchronous environments
- Clear communication with technical and business audiences; connecting team priorities to company outcomes and using evidence to guide decisions
BONUS: BigQuery, dbt Cloud, Looker/LookML, GCP, Python, Go, or infrastructure as code experience; data agents, governed natural-language analytics, semantic layers for AI, or production ML data capabilities; AI-assisted development workflows with effective testing; healthcare or regulated industry experience including sensitive-data governance.
