Senior Analytics Engineer

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. Review code, share reusable patterns, and use AI-assisted development tools to improve delivery while remaining accountable for correctness, privacy, and maintainability.

Must-Haves

• 5+ years of experience in analytics engineering, data engineering, or a related technical role, with a track record of shipping and supporting production data solutions.

• Advanced SQL skills and strong data-modeling judgment. You can reason about join cardinality, aggregation, historical state, and event sequences, and recognize when a query produces plausible but incorrect results.

• Substantial hands-on experience with dbt, including modular transformations, incremental models, testing, documentation, and dependency management.

• Experience developing LookML views, Explores, dimensions, and measures, including diagnosing join behavior and keeping metric definitions consistent across reporting experiences.

• Experience with BigQuery or a comparable cloud data warehouse, including query optimization and practical tradeoffs between freshness, performance, and cost.

• Strong software engineering practices for data work, including version control, code review, automated testing, and controlled deployments. Working proficiency in Python or Go for scripting, validation, and automation.

• Ability to translate ambiguous business requirements into reusable technical solutions. You can ask the questions needed to establish definitions, explain tradeoffs clearly, and carry implementation through to completion.

• Strong debugging skills and experience tracing data issues across source systems, transformation pipelines, and BI tools. You take responsibility for the reliability and usability of what you ship.

• Practical experience using AI-assisted development tools, with the judgment to validate generated SQL, code, and documentation against actual data behavior.

Bonus Points

• Experience migrating analytical models or BI platforms, including reconciliation, compatibility management, and legacy decommissioning.

• Experience building semantic layers, governed natural-language analytics, or data tools for AI agents, including evaluating the correctness of generated answers.

• Experience preparing reusable datasets for ML or partnering with Applied AI teams on data quality, historical consistency, and feature definitions.

• Experience with GCP, dbt Cloud, orchestration tools such as Airflow or Dagster, or automated data observability.

• Experience working with healthcare or other sensitive data, including access controls, PHI/PII handling, and auditability.

90 Day Plan

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

• By Day 30: Understand our data architecture, development workflow, and key business definitions. Trace a core dataset from its source through dbt and Looker, and ship an initial improvement with tests and documentation.

• By Day 60: Own and deliver a meaningful analytical model or self-service capability. Validate its results with stakeholders, address important edge cases, and demonstrate an improvement in correctness, usability, or performance.

• By Day 90: Deliver a defined migration component or reusable data product into production, including monitoring and downstream validation. Establish a pattern other engineers or analysts can reuse, and contribute to a scoped agent or AI data-enablement use case as roadmap priorities allow.

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

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