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HealthLeap

Senior Data Engineer

San Francisco, USAHybridPosted 1 week ago

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Job type
Full-time
Work mode
Hybrid
Level
Senior
Department
Data and Analytics
Experience
5+ years experience
Posted
Sep 24, 2026

About the role

About the role

HealthLeap runs on data. We’re live at 40+ hospitals and plan to add another 100. You’ll build the core pipelines that move messy clinical data from hospital systems into model inputs, analytics, and the APIs behind what clinicians see.

You’ll make that data usable and reliable. When a feed arrives late, a field changes, or a number looks wrong, you’ll trace it through the system and fix the cause.

What you’ll do

  • Build and operate pipelines from hospital ingestion through transformation and delivery.
  • Produce trusted data for ML pipelines, customer analytics, and user-facing APIs.
  • Define data contracts and checks that catch missing records, schema changes, and incorrect values.
  • Handle backfills, late data, failures, and recovery.
  • Work with integration, ML, and product engineers to get data reliably where it needs to go.

What we’re looking for

  • 5+ years building production data systems, with strong Python and SQL.
  • Experience owning pipelines that depend on messy, changing external data.
  • Strong data modeling and judgment about correctness, monitoring, and recovery.
  • The ability to trace a problem across systems and own the fix through production.

What will make you stand out

  • Experience building data pipelines for ML products.
  • Experience with clinical data, EHRs, HL7, or FHIR.
  • Early-stage experience building and operating core data systems.

This role is NOT for you if

  • You want to own one piece of the data stack. You’ll work across ingestion, model inputs, analytics, and product APIs.
  • You want predictable 9-to-5 hours. We protect deep rest, but a hospital go-live can mean a 60+ hour week.

Interview process

No LeetCode or puzzles. Use the tools you’d use on the job, including AI.

  1. Intro call
  2. Data pipeline design
  3. Practical data exercise
  4. Onsite with the team in San Francisco

We decide the same week as the onsite.

Location

San Francisco, in person. We work together in the office by default, with flexibility to work from home when needed. We judge output, not hours.

If you're passionate about applying frontier AI to real-world impact, join us in building healthcare's future.