Senior Data Engineer

HealthLeap is building the data infrastructure that brings messy clinical information into reliable model inputs, analytics, and user-facing APIs. If you care about data trustworthiness and want to own production pipelines end to end, this role offers meaningful equity, comprehensive benefits, and a team that focuses on output over hours.

Location: San Francisco, CA (onsite)

Compensation: USD 175,000 - 275,000 per year

What you’ll build

In this role, you will build and operate core data pipelines that start at hospital ingestion and continue through transformation and delivery. You will ensure clinical data becomes trustworthy inputs for ML pipelines, customer analytics, and user-facing APIs by putting guardrails in place across the full lifecycle of data quality and reliability.

Responsibilities

• 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 implement checks to catch missing records, schema changes, and incorrect values.
• Handle backfills, late-arriving data, pipeline failures, and recovery.
• Collaborate with integration, ML, and product engineers to deliver reliable data to where it is needed.

What you bring

• 5+ years building production data systems, with strong Python and SQL.
• Experience owning pipelines that depend on messy, changing external data.
• Strong data modeling skills and judgment focused on correctness, monitoring, and recovery.
• Ability to trace issues across systems and drive fixes through production.

Technologies

• Python
• SQL

Benefits

• $175,000–$275,000 base plus meaningful equity
• 100% covered healthcare premiums
• Unlimited PTO with a 20-day minimum
• 4% 401(k) match
• Laptop and home office budget

What will help 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.

Interview process

• No LeetCode or puzzles; you’ll use the tools you would on the job, including AI.
• Intro call
• Data pipeline design
• Practical data exercise
• Onsite with the team in San Francisco
• Decision made the same week as onsite

How the work is shaped

This role is not a fit if you only want to own a single part of the data stack. You will work across ingestion, model inputs, analytics, and product APIs. You should also be comfortable with flexible time expectations when hospital go-lives require it, including potential 60+ hour weeks, even though deep rest is protected.