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 automation where they reduce toil and improve delivery, with validation that protects data correctness. Use adoption, migration progress, reliability, and stakeholder feedback to assess impact.

Requirements:

• 3+ years of engineering management experience, including direct leadership of both data engineering and analytics engineering work. Deep expertise in one discipline is sufficient if you have successfully led and developed engineers in the other.

• Strong technical judgment across data pipelines, cloud warehouses, SQL and data modeling, transformation frameworks, orchestration, and BI or semantic layers. You can review designs and code, investigate difficult problems, and make sound tradeoffs with experienced engineers.

• 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 relying on a dedicated product manager. You can understand a business problem, challenge assumptions, and turn competing requests into an achievable plan.

• A track record of building reusable capabilities and helping other teams adopt them. You can balance shared standards with the needs of different business domains.

• Experience hiring, coaching, and managing performance across different engineering skill sets, and building collaborative teams in a remote or asynchronous environment.

• Clear communication with technical and business audiences, with experience connecting team priorities to company outcomes and using evidence to guide decisions.

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.

90 Day Plan

An example of what your first 90 days at Virta could include is:

• By Day 45: Build relationships with the team and its stakeholders, understand the migration and operational workload, and establish individual growth goals. Assess capability gaps across both disciplines and define profiles for additional hires.

• By Day 60: Align stakeholders on the next migration milestones and the highest-value self-service and agent/ML opportunities. Establish success measures, clarify ownership with Applied AI and MLOps, and begin delivery on a prioritized improvement.

• By Day 90: Deliver a meaningful migration milestone or self-service improvement with measurable user adoption. Align the next two quarters of work in the team’s Now/Next/Later roadmap, with a clear path for data agents and ML enablement, and advance hiring against the agreed team needs.

Values-driven culture

Virta’s company values drive our culture, so you’ll do well if:

• You put people first and take care of yourself, your peers, and our patients equally

• You have a strong sense of ownership and take initiative while empowering others to do the same

• You prioritize positive impact over busy work

• You have no ego and understand that everyone has something to bring to the table regardless of experience

• You appreciate transparency and promote trust and empowerment through open access of information

• You are evidence-based and prioritize data and science over seniority or dogma

• You take risks and rapidly iterate

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As part of your duties at Virta, you may come in contact with sensitive patient information that is governed by HIPAA. Throughout your career at Virta, you will be expected to follow Virta's security and privacy procedures to ensure our patients' information remains strictly confidential. Security and privacy training will be provided.

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