# Senior Analytics Engineer at Virta Health

Virta Health is hiring a Senior Analytics Engineer for a remote role open to applicants in United States. It pays $162k-$209k a year and Level rates it Little AI ●○○○; you can [apply on Virta Health](https://jobsbylevel.com/go/792cb81e-ad90-40ae-8944-a50c393215da?utm_medium=ai-agent).

AI Level 1, AI centrality 11 out of 100. Remote (Remote).

## Details

- Company: [Virta Health](https://jobsbylevel.com/companies/virta-health)
- AI level: AI Level 1 (score 11 out of 100)
- Location: Remote (Remote)
- Salary: $162k-$209k
- Posted: October 30, 2026
- Apply: https://jobsbylevel.com/go/792cb81e-ad90-40ae-8944-a50c393215da?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-analytics-engineer-at-virta-health-bece51

## Prepare for this job

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## Description

Virta Health is on a mission to transform metabolic health care. 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 diabetes and obesity in one billion people. About the Role We’re looking for a Senior Analytics Engineer to build the trusted data models and analytics capabilities that help Virta operate and improve care at scale. This is a hands-on technical role working alongside other data & analytics engineers with both engineering and business stakeholders. You will write SQL and dbt models, develop shared LookML, investigate data discrepancies, and ship tested solutions into production. You’ll work through questions such as how to represent a member’s multiple enrollment journeys, keep a metric consistent across business teams, or make a dataset useful for several downstream consumers. Your work will connect source-system behavior to clear, reliable business meaning. Our stack includes BigQuery, dbt Cloud, and Looker/LookML. Near-term priorities include advancing our data and Looker modernization, expanding self-service analytics, and building governed data capabilities & a semantic layer for agents and AI. You will partner with experienced teammates and stakeholders to understand requirements, make technical decisions, and own implementation through rollout and ongoing support. Responsibilities Build reusable analytical models. Design and implement dbt models and BigQuery datasets that represent core business concepts accurately. Make deliberate choices about grain, keys, relationships, and historical behavior so the same models can support multiple use cases. Develop shared semantics and self-service analytics. Build and maintain LookML views, Explores, measures, and supporting dashboards. Keep business logic consistent across dbt and Looker, with clear definitions and documentation that help users answer questions independently. Deliver modernization work into production. Implement defined components of our data rearchitecture and Looker Core migration. Reconcile results against existing systems, explain intentional differences, validate downstream dependencies, and help retire legacy models and content. Turn business questions into technical solutions. Work directly with Analytics, Finance, Growth, Customer Success, and Clinical partners to understand how data will be used. Clarify definitions and edge cases, then build solutions that meet the immediate need and can be reused. Own data correctness and operational quality. Implement automated tests for model behavior, relationships, freshness, and business rules. Investigate discrepancies across source data, transformations, and BI outputs, resolve production issues, and add safeguards against recurrence. Improve performance and maintainability. Optimize SQL, materializations, incremental processing, and BigQuery partitioning and clustering. Simplify duplicate logic and improve model structure, documentation, and lineage to make changes safer and easier to understand. Build data capabilities for agents and AI. Contribute governed datasets, semantic definitions, and contextual documentation that help agents use data correctly. Partner on data-agent implementations and validate their answers against trusted queries and metrics. Support Applied AI with reliable analytical data and clear interfaces for its ML workflows. Raise the engineering bar through execution. Own work from technical scoping through deployment and iteration.

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