Staff Software Engineer

Astrana Health, Inc.•Alhambra, CA

11d•Remote

About The Position

Astrana Health builds the technology that lets physician groups deliver value-based care: the systems that enables physicians to deliver higher quality care and keeps its payers, and its partners in sync. Our engineers work on anything from eligibility, claims, and authorizations data, integrations with payer and provider systems, clinical integrations, provider-facing tools, and the data platform that ties them together. The pace varies with the work. Some of it is fast iteration with real users. Some of it touches PHI, billing, or clinical decisions and has to be right the first time. A staff engineer sets the example for knowing which is which, and for building systems that make it safe to move fast where fast is appropriate. We expect engineers to work across the whole stack. This is a hands-on role: most of a staff engineer's time is spent designing and building, and the rest is spent making other engineers' building better. As a Staff Engineer you own a technical domain that cuts across product surfaces: the data platform, the integration layer, the clinical data model, or the shape of our services. You choose the structure everyone else builds inside, you own the consequences of those choices over years, and you are accountable for the technical quality of decisions made by people you do not manage. Your judgment is a company asset, and we expect you to use it out loud. Our Values: Put Patients First Empower Entrepreneurial Provider and Care Teams Operate with Integrity & Excellence Be Innovative Work As One Team

Requirements

8+ years of professional software engineering experience, including years owning systems that other engineers build on.
Four-year undergraduate degree in Computer Science, Mathematics, Physics, or Engineering. Rare exceptions for outstanding experience.
Full-stack experience. You have owned systems from the data layer through the API to the UI, and building is the bulk of your work.
Strong TypeScript and strong SQL, with deep experience in data modeling and data-heavy systems.
Data and analytics savvy. You have designed data for both transactional and analytical use, and you can look at a report and tell whether the numbers are plausible.
Production experience with React.
Docker and container-based deployment.
Hands-on ownership of infrastructure on at least one major public cloud (Azure, AWS, or GCP), including the cost and operational tradeoffs.
Strong proficiency with AI development tools such as coding agents and assistants. You use them to multiply your own and your team's output, hold their output to the same standard as any other code, have clear judgment about where they help and where they create risk, and can set that standard for other engineers.

Nice To Haves

Database modeling and migration frameworks such as Drizzle, Prisma, SQLAlchemy, or Alembic, including experience choosing one and setting conventions for a team.
Depth across more than one of Azure, AWS, and GCP, including cloud IAM, role design, and least-privilege access at the organization level.
Python.
Experience designing LLM-driven systems in production: tool use, retrieval-augmented generation, evaluation, cost control, and safety.
Healthcare data experience: eligibility, claims, clinical data, HL7/FHIR, or payer/provider integrations at multi-tenant scale.
Experience with HIPAA compliance, PHI handling, and audit requirements in system design.

Responsibilities

Set technical direction for your domain: what we build on, what we migrate off, and what we deliberately tolerate.
Make and defend architecture and infrastructure choices on cost, operational burden, hiring implications, and exit cost as well as capability.
Build the hard core of new systems yourself, and the reference implementations and tooling that let other engineers move fast inside them.
Evolve existing systems: decomposition, strangler patterns, deprecations, and migrations off old paths that actually finish.
Design for our failure domains. Know what happens when a partner feed dies, a vendor deprecates an API, or a region goes down, and what the recovery path is.
Own the shape of our data. Decide how eligibility, claims, clinical, and quality data are modeled, joined, and served for both product features and analytics, and make it easy for engineers and analysts to get trustworthy answers out of it.
Set cross-cutting standards (schema conventions, service templates, CI/CD, environment and test-data strategy) and get them adopted.
Turn recurring classes of incidents into structural fixes.
Manage technical debt as a portfolio: what we carry, what it costs us, and what we pay down next.
Grow the senior bench: run technical interviews, calibrate hiring and promotion decisions, and develop senior engineers so more of them operate at a staff level.
Say the unpopular thing, early and clearly, to engineering and non-engineering leadership alike.