Metriport
Staff Data Engineer
San Francisco · Staff+
Sponsorship not specifiedDetected 1 day ago
TypeScriptPythonNode.jsCode ReviewPostgreSQLDynamoDBBigQuerySnowflakeRedshiftAWSKafkaMachine LearningSparkAirflowdbtData EngineeringData ScienceAgileHL7/FHIREHR/EMRICD-10Mentoring
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16Unrated
Cap-exempt (no lottery)0
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Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- We integrate with all major US healthcare IT systems and tap into comprehensive medical data for 300+ million individuals.
- We're a tight-knit, high-performing team of mostly former founders (including two YC alumni).
- We're engineering-heavy, operate with minimal bureaucracy and high autonomy, and hire based on competence, not prestige.
Responsibilities
- Driving the critical data projects end-to-end: writing Design Documents, shipping v0's quickly, and iterating to v1 and beyond.
- Multiplying the team: mentoring engineers on data fundamentals, reviewing designs and PRs, and judging when to invest in quality vs. ship fast.
- Eventually, acting as Team Lead for a group of engineers: breaking down and delegating work, unblocking teammates, and owning your team's delivery - while staying hands-on in the code.
- Example projects you could own:
- Building document-processing pipelines that extract structured data from PDFs, images, and free text to feed ML models.
- Experience mentoring or guiding other engineers - through code reviews, pairing, design feedback, or onboarding.
Requirements
- Experience leading engineers.
Skills
- FHIR, HIE, IHE, EHR/EMR, NPI, TEFCA, ADT, HL7, HEDIS, RAF, SNOMED, LOINC, ICD-10, etc.
- Competitive equity + compensation package 🚀
- Full family Platinum health insurance, dental, and vision coverage 🦷
- 401(k) retirement plan + matching 💰
- Flexible work from home or in-office 🏢
- Healthy lunches are complimentary when working in-office (and breakfast + dinners as needed) 🍏
- Quarterly company off-sites with the team ⛷️
- MacBook provided by us 💻
- Unlimited PTO (we work hard, but trust you to take time you need to be at your best) 🧘♂️
- Core business logic in Node.js and TypeScript, with Python in data and ML workflows.
- AWS across the board (ECS, Lambda, SQS, SNS, Batch, etc.), infrastructure as code with CDK.
- Data lives in S3, PostgreSQL/Aurora, DynamoDB, Snowflake, and our FHIR server - with Athena for querying S3 and SageMaker for ML.
Compensation
- Competitive equity + compensation package 🚀
Company info
- We've found product-market fit with multi-million ARR, 100+ customers (including Amazon One Medical, Strive Health, Circle Medical, and Brightside Health), backing from top VCs, and years of runway.
- We push hard-founders work six days a week from our SF office-but give everyone freedom to craft their schedule.
- We measure output and we're committed to sustainable intensity.
- Metriport is an open-source data intelligence platform that helps healthcare organizations access and exchange patient data in real-time: record retrieval that used to take days now takes seconds, and clinicians walk into every appointment with the full patient picture.
- We're ready to scale.
- We're looking for a data engineering leader who can own our data platform end-to-end:
- You've designed and operated data platforms at scale, with broad hands-on experience across the ecosystem - distributed processing, lakehouses, warehouses, streaming, orchestration - and people usually come to you for technical guidance.
- You've led engineers before - as a team lead, tech lead, or founder - and you own outcomes: technical direction, results, and the work of the people you lead. You're energized by multiplying a team's output, not just your own.
- You're entrepreneurial-minded with an olympian-level work ethic (about half our engineering team are former founders).
- You own data reliability, quality, and governance as first-class parts of delivery.
- You care about delivering value to customers, not about what frilly new tech is under the hood.
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This listing is sourced directly from Metriport's careers page and normalized into a canonical job model.