Arcadia's data platform powers population health analytics for health plans, ACOs, and provider groups across the country. As a Lead Analytics Engineer — Data Modeling & Quality, you sit at the intersection of data quality ownership and analytical data modeling. You'll own the SQL and DBT layer that transforms raw clinical and claims data into trusted, production-grade datasets, while also serving as the quality authority for the data those models produce.
This is a hybrid role — deeper SQL and DBT expertise than a traditional Data Health Professional, with a more analytical and model-focused scope than a Data Engineering role. You're less focused on pipeline infrastructure and more on the logic, shape, and trustworthiness of the data itself.
- Independently triage and resolve pipeline data quality issues
- Author at least one new DBT model or refactor an existing one to meet current modeling standards
- Design a DBT test suite for a set of models lacking coverage
- Understand the end-to-end pipeline from ingress through silver and gold, and be able to trace a data quality issue to its root layer
- Building strong working relationships with clients and cross-functional partners (Data Engineering, Customer Success)
- Deeply familiar with Arcadia's full data stack — from ingress through silver, gold, and downstream consumers
- Driving at least one improvement project forward, whether technical (e.g. model refactor, new DQ framework) or process-focused (e.g. promotion playbook, triage workflow)
- Recognized as a leader within the department — peers and stakeholders seek out your expertise on data modeling and quality
- Operating independently across the full scope of the role with minimal guidance
- Two or more improvement projects completed and in production, with measurable impact on data quality or operational efficiency