Senior Analytics Engineer

Virta Health is hiring a Senior Analytics Engineer to build trusted data models and analytics capabilities that help the company operate and improve care at scale. The role develops dbt and BigQuery models, shared LookML semantics, and governed data capabilities while partnering with business and engineering stakeholders to deliver reliable, tested solutions.

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

Requirements:

• 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
• 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