Senior Analytics Engineer

The Opportunity Nest Health sends clinicians into people’s homes. We are the primary care provider for entire households: families managing chronic conditions, caregivers juggling complex needs, and people who can’t easily get to a clinic. Every visit generates data that helps us understand households and improve care over time. After two years of operating in the field, we’ve built a data warehouse and production models that span claims, care gaps, household-level data, and a live ML pipeline. We’re hiring a Senior Analytics Engineer to strengthen this foundation and build what’s next. What you’ll own End‑to‑end dbt project: documentation, testing, dev/prod separation, clean schema design, and data‑quality governance to support clinical AI. Semantic layer: metric definitions, data‑quality standards, and governance processes for self‑service analytics. Data observability: monitoring pipelines, alerting on anomalies, and coverage to prevent silent failures before a home visit. Orchestration layer: evaluate and implement a pipeline solution on AWS to replace Azure Data Factory. What you’ll work on Our stack is Snowflake, dbt, Python, moving to AWS. Clinical data from our EHR arrives via a native Snowflake Data Share; we have multi‑payer claims, care gap data, family‑unit models, and an outreach data layer. You’ll support a Data Scientist running production models and the roadmap into broader clinical AI. Work with clinical, operations, and finance stakeholders on metric definitions and data quality that shape both day‑to‑day decisions and long‑term platform strategy. What you bring Strong dbt fundamentals: testing, documentation, and downstream‑consumer focus. Depth in Snowflake: schema design, query performance, warehouse cost management, and governance. AWS orientation: experience migrating to AWS and data engineering in a HIPAA‑aligned environment. Pipeline orchestration: experience with Airflow, Prefect, Dagster, or comparable tools. Python fluency: comfortable reading, writing, and debugging—using AI tools when helpful. Healthcare data literacy: understanding of eligibility files, care gaps, and distinctions between payer claims and clinical encounter data; Medicaid experience is a real advantage. Interest in AI: care about building the infrastructure that enables trustworthy clinical AI systems. Stakeholder communication: translate data insights to non‑technical leaders and earn their trust. What this is not Dashboard‑building role. We rely on self‑service analytics; your work will be the foundation those tools depend on. People‑management role today. This is an individual contributor position, though the person who builds it could lead the data team as the company scales. Experience 5+ years owning the transformation layer of a modern data stack. Production dbt, strong Snowflake governance, and Python proficiency. Preferred: AWS experience or rapid ramp‑up capability. Plus: experience with Medicaid value‑based care operations, CRM & EHR data, and familiar workflows. Interest in meaningful AI application beyond productivity. We are an equal‑opportunity employer, indiscriminative of race, religion, ethnicity, national origin, citizenship, gender, gender identity, sexual orientation, age, veteran status, disability, genetic information, or any other protected characteristics. #J-18808-Ljbffr