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Senior Analytics Engineer
About this role
Virta Health is seeking a Senior Analytics Engineer to build data models and analytics capabilities to support care at scale. The role involves writing SQL and dbt models, developing LookML, investigating data discrepancies, and delivering production-ready solutions. You will work with engineering and business stakeholders to represent member enrollment journeys, align metrics across teams, and create datasets useful for multiple downstream consumers. The position uses a stack including BigQuery, dbt Cloud, and Looker/LookML, with a focus on data modernization, self-service analytics, and governed data capabilities. The employer describes a remote-first setup and offers a compensation range of 161,500 to 209,000 USD, with a focus on reliability, privacy, and maintainability in delivery.
What you would do
- Build reusable analytical models by designing dbt models and BigQuery datasets
- Develop shared semantics, LookML views, Explores, and dashboards
- Deliver modernization work by implementing components of data rearchitecture and Looker Core migration
- Translate business questions into technical solutions with cross-functional partners
- Own data correctness and operational quality through automated tests and issue resolution
- Improve performance and maintainability through SQL optimisation and efficient data processing
- Build data capabilities for agents and AI, including semantic definitions and validated interfaces
- Contribute to engineering excellence by scoping, deployment, code reviews, and reusable patterns
What they are asking for
- 5+ years in analytics engineering, data engineering, or related roles with production data experience
- Advanced SQL skills and strong data-modeling judgement
- Hands-on experience with dbt, including modular transformations, incremental models, testing, and documentation
- Experience developing LookML views, Explores, dimensions, and measures with consistent metric definitions
- Experience with BigQuery or similar cloud data warehouse and tradeoffs between freshness, performance, and cost
- Software engineering practices for data work, including version control, automated testing, and controlled deployments
- Proficiency in Python or Go for scripting and automation
- Ability to translate ambiguous business requirements into reusable technical solutions
- Strong debugging skills and experience tracing data issues across sources and pipelines
- Experience with AI-assisted development tools and validating generated outputs against data
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