A fast-scaling healthcare AI company is building an agentic AI platform for healthcare finance. This role sits between a customer's financial and clinical data and the AI systems running on top of it. You will reverse-engineer customer-specific business rules, from payer contract logic to chart-of-accounts idiosyncrasies, and build the transformations that map raw customer data into a unified ontology and semantic layer.
Analytics engineering is the source of truth the whole platform runs on. Without it being right, there is no AI and no trust. You will feel at home here if you know healthcare data well enough to spot when a number is wrong before anyone else does.
What You'll Do
Review customer data models. Sit with finance and clinical teams, sometimes on site, to capture their source-of-truth logic firsthand.
Codify business rules. Turn under-documented payer contract and chart-of-accounts logic into durable, tested transformations.
Build in SQL. Map customer data into the unified ontology and semantic layer inside the managed pipeline.
Own correctness. You hold the testing and validation bar on everything you ship.
Expand the ontology. Work with insights and product engineering as new customer needs and data sources come in.
- Hands-on healthcare data experience in claims, reimbursement, revenue cycle, or health system finance
- Built or owned a data pipeline or platform end to end (greenfield): designed and shipped it, not only maintained it or handed specs to someone else
- Authored the transformation layer: SQL or dbt models that encode business logic (claims, remit, or revenue-cycle rules) into analytics-ready tables you own
- Strong technical skills in SQL, Python, and dbt, with proven pipeline-building experience
- 5+ years of experience in data or analytics engineering
- Customer or stakeholder-facing technical work (forward deployed, integrations, or solutions engineering)
- Worked in a fast-paced healthcare environment: health tech startup, health system, or healthcare consulting
- Bachelor's or higher in a quantitative or business-adjacent field, or equivalent hands-on healthcare data experience
- Experience building pipelines independently, not just maintaining them