Staff Software Engineer, Full Stack (Enabling Plan and Benefits Data)

Sidecar Health is redefining health insurance. Our mission is to make excellent healthcare affordable and accessible for everyone. We know that to accomplish this lofty mission, we need driven people who will make things happen.
The passionate people who make up Sidecar Health’s team come from all over, with backgrounds as tech leaders, policy makers, healthcare professionals, and beyond. And they all have one thing in common—the desire to fix a broken system and make it more personalized, affordable, and transparent.
If you want to use your talents to transform healthcare in the United States, come join us!
About the Role
Healthcare runs on data moving between systems that were never designed to talk to each other. Provider directories, benefit and pricing feeds, claims data, and a long tail of third-party partners each with their own format and quirks. You'll own the platform that makes that data usable: the pipelines that land it in Snowflake, the delivery pipelines that gets it back out to partners, and the third-party integrations that keep it flowing correctly at scale.
You'll also build the internal tools that let our operations and pricing teams move fast without waiting on engineering for every change. Things like automating pricing adjustments for benefit amounts across large provider datasets, so repetitive manual work becomes a self-serve, auditable workflow.
This is a Staff-level role. You'll be expected to set technical direction for how we handle data at scale, unblock other engineers who are heads-down on features, and be the person the team turns to when a data pipeline or integration problem doesn't have an obvious answer.
**MUST BE BASED IN CALIFORNIA, OREGON OR WASHINGTON**
What You'll Do
Own and evolve the data pipeline platform that ingests provider, benefit, and pricing data and delivers it into Snowflake and out to third-party consumers via SFTP
Design, build, and maintain integrations with third-party data providers and vendors, handling schema drift, malformed data, and partner-specific quirks gracefully
Build internal staff tools that automate repetitive, high-volume operational work (e.g., pricing adjustments for benefit amounts) — turning manual processes into scalable, self-service systems
Process and reconcile large volumes of medical provider data, with an eye for data quality, lineage, and auditability
Set technical direction for data infrastructure and integration patterns — evaluate and introduce modern architectural approaches (event-driven pipelines, CDC, workflow orchestration, etc.) where they solve real problems
Act as a technical unblocker across teams: review designs, pair on hard problems, and help other engineers ship rather than stall
Drive engineering standards for data pipeline reliability, observability, and testing
Partner closely with product, operations, and pricing stakeholders to translate ambiguous business needs (like "automate this pricing process") into durable technical systems
Mentor engineers and raise the technical bar through code review, design docs, and hands-on collaboration
What You'll Bring
10+ years of professional software engineering experience, with a track record of owning data-intensive systems in production
Deep experience building and operating data pipelines at scale; ingestion, transformation, and delivery (Snowflake experience strongly preferred)
Hands-on experience building and maintaining third-party integrations, including handling unreliable or inconsistent external data
Experience building internal tools/platforms that automate operational workflows for non-engineering teams
Strong fundamentals in distributed systems, data modeling, and modern architectural patterns for processing data at scale
Demonstrated ability to operate as a technical leader: setting direction, unblocking others, and influencing without direct authority
Comfort with ambiguity; you can take a loosely defined operational pain point and turn it into a well-scoped technical solution
Strong communication skills; able to explain a data architecture tradeoff to both engineers and non-technical stakeholders
Fluent, disciplined use of AI-assisted tooling to move faster, with the rigor to verify correctness, security, and quality before it ships
Nice to haves:
Experience with healthcare data (provider directories, NPI, claims, fee schedules, or benefits data)