Senior Analytics Engineer

Virta Health

5d • $161,500 - $209,000 • Remote

About The Position

Virta Health is seeking a hands-on Senior Analytics Engineer to build trusted data models and analytics capabilities that help the company operate and improve care at scale. The role works alongside data and analytics engineers and business stakeholders, writing SQL and dbt models, developing shared LookML, investigating data discrepancies, and delivering tested solutions to production. The work connects source-system behavior to clear, reliable business meaning, including representing enrollment journeys, establishing consistent metrics, and building datasets for multiple downstream uses.

Virta’s stack includes BigQuery, dbt Cloud, and Looker/LookML. Near-term priorities include data and Looker modernization, expanded self-service analytics, and governed data capabilities and a semantic layer for agents and AI. The engineer will partner with teammates and stakeholders to understand requirements, make technical decisions, and own implementation through rollout and ongoing support.

The role includes handling sensitive patient information governed by HIPAA. Employees are expected to follow Virta’s security and privacy procedures; training is provided. Virta is a remote-first company with office hubs in Denver and San Francisco. Corporate roles are not hired in AK, DE, HI, ME, MS, OK, SD, or VT.

Requirements

• At least 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, including reasoning about join cardinality, aggregation, historical state, and event sequences.
• Substantial hands-on dbt experience, including modular transformations, incremental models, testing, documentation, and dependency management.
• Experience developing LookML views, Explores, dimensions, and measures, diagnosing join behavior, and maintaining consistent metric definitions across reporting experiences.
• Experience with BigQuery or a comparable cloud data warehouse, including query optimization and tradeoffs among freshness, performance, and cost.
• Strong data software-engineering practices, 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, clarify definitions, explain tradeoffs, and carry implementation through to completion.
• Strong debugging skills and experience tracing data issues across source systems, transformation pipelines, and BI tools.
• Practical experience using AI-assisted development tools and judgment to validate generated SQL, code, and documentation against actual data behavior.

Nice To Haves

• 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.

Responsibilities

• Design and implement reusable dbt models and BigQuery datasets that accurately represent core business concepts, with deliberate choices about grain, keys, relationships, and historical behavior.
• Build and maintain LookML views, Explores, measures, and supporting dashboards, keeping business logic consistent across dbt and Looker and documenting definitions for self-service use.
• Implement defined components of data rearchitecture and the Looker Core migration; reconcile results, explain intentional differences, validate downstream dependencies, and help retire legacy models and content.
• Partner with Analytics, Finance, Growth, Customer Success, and Clinical stakeholders to clarify data requirements, definitions, and edge cases, then deliver reusable technical solutions.
• Implement automated tests for model behavior, relationships, freshness, and business rules; investigate discrepancies and production issues and add safeguards against recurrence.
• Improve SQL, materializations, incremental processing, and BigQuery partitioning and clustering; simplify duplicate logic and improve model structure, documentation, and lineage.
• Contribute governed datasets, semantic definitions, and contextual documentation for agents and AI; partner on data-agent implementations and validate answers against trusted queries and metrics.
• Support Applied AI with reliable analytical data and clear interfaces for ML workflows.
• Own work from technical scoping through deployment and iteration, review code, share reusable patterns, and use AI-assisted development tools while maintaining accountability for correctness, privacy, and maintainability.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed