Engineering Manager, Data Engineering

Virta Health

23h•$175,000 - $228,000•Remote

About The Position

Virta Health is seeking an Engineering Manager for Data Engineering to lead and grow the team responsible for the data foundations supporting its business, care delivery, and AI capabilities. The established team includes Data Engineers focused on the platform and Analytics Engineers focused on models. The manager will develop both disciplines and their distinct career paths while being accountable for reliable pipelines, platform tooling, engineering standards, reusable analytical models, semantic definitions, and Looker experiences that support decision-making. Initial priorities include completing data and analytics modernization, expanding self-service, and enabling agents and machine learning to use trusted data. The technology stack includes BigQuery, dbt Cloud, and Looker/LookML. The manager will work directly with stakeholders to understand needs, set priorities, measure adoption, and contribute technical judgment when needed. The role includes leading the remaining BigQuery medallion architecture and Looker Core rollout, preserving confidence in business metrics, and retiring legacy paths. It also owns stakeholder discovery and roadmap delivery, shared data and analytics capabilities, governance and reliability, and data capabilities for agents and ML. The manager will collaborate with Engineering, Analytics, Finance, Commercial, Clinical, Security, Infrastructure, Applied AI, and MLOps partners. Virta is a remote-first company with office hubs in Denver and San Francisco. The role may involve access to sensitive patient information governed by HIPAA; employees are expected to follow security and privacy procedures, and training is provided. Virta uses location-based compensation. The stated compensation range is $175,000–$228,000, based on qualifications and experience.

Requirements

At least 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 the candidate has 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.
Ability to 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.
Ability to understand business problems, challenge assumptions, and turn competing requests into an achievable plan.
Track record of building reusable capabilities and helping other teams adopt them while balancing shared standards with different domain needs.
Experience hiring, coaching, and managing performance across different engineering skill sets.
Experience building collaborative teams in a remote or asynchronous environment.
Clear communication with technical and business audiences, connecting team priorities to company outcomes and using evidence to guide decisions.

Nice To Haves

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.

Responsibilities

Lead, coach, and grow Data Engineers and Analytics Engineers; set expectations and support technical and career development.
Assess team capability needs and use planned hires to build the right mix of skills.
Complete the data and analytics migration, including the remaining BigQuery medallion architecture and Looker Core rollout, validation, stakeholder onboarding, and retirement of legacy paths.
Preserve confidence in business metrics throughout the migration.
Build relationships with Engineering, Analytics, Finance, Commercial, and Clinical stakeholders; translate needs into a focused roadmap and balance competing priorities with operational support.
Expand self-service through reusable ingestion and transformation patterns, tooling, documentation, and standards.
Guide development of reusable dbt models, shared semantic definitions, and LookML in partnership with domain experts.
Build and operate data agents, and provide governed datasets, semantic context, and tools for agents developed by other teams.
Partner with Applied AI and MLOps on data access, quality, and interfaces for ML workflows.
Establish data ownership, testing, freshness expectations, actionable alerts, and incident practices.
Partner with Security and Infrastructure on access controls, PHI/PII protection, lineage, and cost management.
Apply AI-assisted development and automation where they reduce toil and improve delivery, with validation to protect data correctness.
Assess impact using adoption, migration progress, reliability, and stakeholder feedback.