[Hiring] Engineering Manager, Data Engineering @Virta Health

Role Description

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. 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.
• 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.
• 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.
• 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.
• 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.
• A track record of building reusable capabilities and helping other teams adopt them.
• Experience hiring, coaching, and managing performance across different engineering skill sets.
• 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.

90 Day Plan

• By Day 45: Build relationships with the team and its stakeholders, understand the migration and operational workload, and establish individual growth goals.
• By Day 60: Align stakeholders on the next migration milestones and the highest-value self-service and agent/ML opportunities.
• By Day 90: Deliver a meaningful migration milestone or self-service improvement with measurable user adoption.

Values-driven culture

• 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.