The OpportunityNest Health sends clinicians into people's homes. We're 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 creates data. Every interaction helps us better understand the household and improve the care we deliver 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. It's not perfectly documented. Governance is still evolving. It's not yet set up to support the AI products we're building. But the foundation is strong.
We're hiring a Senior Analytics Engineer to help us strengthen that foundation and build what's next.
What you'll own- You'll own the dbt project end to end. That means documentation, testing, dev/prod separation, clean schema design, and making the transformation layer trustworthy enough to support clinical AI. In the near term, much of the work will center on moving the platform to AWS. Both require meaningful data engineering judgment. The first 90 days will include auditing what exists, documenting what matters, and fixing what's broken. The payoff is a data platform that can support the clinical products we're building over the next few years.
- You'll own the semantic layer. We're betting on self-service analytics as the primary way the business answers questions. Self-service only works if the semantic layer underneath it is solid, so you'll own the metric definitions, data quality standards, and governance processes that make self-service trustworthy.
- You'll own data observability. We need to know about a broken data ingest process before a clinician does. That means monitoring pipelines, alerting on anomalies, and building enough coverage that a silent failure doesn't make it into a home visit.
- You'll own the orchestration layer. We run pipelines on ADF today; moving to AWS means that goes away and something replaces it. Evaluating the options, making the call, and building it out is yours.
What you'll work on- Our stack is Snowflake, dbt, Python, shifting 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. The data platform has real clinical consequences: a bad pipeline puts wrong information in a clinician's hand before a home visit.
- We have a Data Scientist running production models today, and the roadmap extends into broader clinical AI. Your work will support both. You'll also work with clinical, operations, and finance stakeholders on metric definitions and data quality. Some of that work will shape the long-term platform. Some of it will support day-to-day operational decisions.
What you bring- Strong dbt fundamentals. You think about testing, documentation, and downstream consumers as part of the definition of done, not afterthoughts. A model that runs isn't a model that's finished. You write tests with real logic: catch a broken join, catch a silent upstream change before a clinician sees wrong data.
- Snowflake depth: schema design, query performance, warehouse cost management, governance. You know what a well-run Snowflake environment looks like and can tell when you're not in one.
- AWS orientation. We're migrating toward AWS and you'll be doing data engineering work as part of that transition. Prior experience is a plus; a clear path to getting there works too.
- Pipeline orchestration. You've worked with Airflow, Prefect, Dagster, or something comparable. You have opinions about the tradeoffs and can make a call on what fits a stack like ours.
- Python fluency. You can read, write, and debug it comfortably. You're comfortable using AI tools as part of your workflow when they help you move faster.
- Healthcare data literacy. You don't need prior healthcare experience, but you need to be able to explain what an eligibility file is, what a care gap means, and why payer claims look different from clinical encounter data. Medicaid experience is a real advantage.
- Interest in AI. We're building clinical AI products, and the data platform is what makes them work. You don't need to be an ML expert, but you should care about building the infrastructure that makes those systems possible and trustworthy.
- Stakeholder communication. This isn't a heads-down engineering role. You'll sit with a CFO or a clinical director, translate what a number means and why it changed, and make them trust the data.
What this is not- This isn't a dashboard-building role. We're betting on self-service analytics for reporting. Your work will be the foundation those tools depend on, not the dashboards themselves.
- Not a people management role today. This is an individual contributor role with real ownership. That said, Nest's data function today is loosely formed; we're tightening it. The person who builds it will be a candidate to lead it as the company scales.
Experience- 5+ years owning the transformation layer of a modern data stack. Not contributing to it, owning it.
- Production dbt. Strong Snowflake governance. Python you can comfortably read, write, and debug.
- AWS experience preferred, or strong ability to ramp quickly, especially in a HIPAA-aligned environment.
- Experience working with how data supports value-based care operations in Medicaid populations is a plus. Similarly, familiarity with CRM & EHR data and workflows.
- Uses AI in day-to-day work and is interested in where it meaningfully changes the system, not just productivity.
How We Work- Fully remote. We hire the best people regardless of location.
- You own what you build. No handoffs. No separate ops function.
- Everyone uses AI. That's not a differentiator here, it's the floor.
- We don't measure what ships. We measure what changes for the families we serve.
