Senior Data Engineer

Senior Data Engineer

San Francisco, CA

Hybrid

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 integrate with all major US healthcare IT systems and tap into comprehensive medical data for 300+ million individuals.

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're ready to scale. 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.

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.

We're looking for a data engineer who can operate at a Senior level:

• You've built and scaled data pipelines and systems, with hands-on experience across the ecosystem — distributed processing, lake houses, warehouses, streaming, orchestration. You know when each is (and isn't) the right tool for the job, and people usually come to you for guidance on how to move, transform, and serve data reliably.

• You fully own your work — technical decisions, delivery, results — and you level up the engineers around you through code reviews, pairing, and guidance. Multiplying the team's output energizes you as much as shipping your own.

• You're entrepreneurial-minded with an olympian-level work ethic (about half our engineering team are former founders).

• You understand 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.

• When someone scopes a project for 3 weeks, you ask "why can't it be done in 3 days?" — and you help others develop that same instinct.

• You're a hacker at heart, with a good sense of which rules should, and shouldn't, be broken.

We ingest clinical data for millions of patients from external healthcare sources, with continuous updates for a growing subset of those patients. You'll be a catalyst to scale the data platform that powers our product — and ship it to customers fast.

Day to day, that looks like:

• Raising the technical bar for our data platform: building on and improving our warehouse, data lake, and ETL/ELT architecture so it scales with patient and customer growth, and helping evaluate the right tools (batch and streaming processing, table formats, orchestration, query engines).

• Driving data projects end-to-end: writing Design Documents, shipping v0's quickly, and iterating to v1 and beyond.

• Supporting AI/ML efforts - making sure the AI Engineers have the data they need.

• Multiplying the team: mentoring engineers on data fundamentals, reviewing designs and PRs, and judging when to invest in quality vs. ship fast.

• Participating in bi-weekly sprint planning and retros, joining our daily 30-min remote stand-up at 7:30am PST (our only mandatory meeting), and taking part in the on-call rotation.

Example projects you could own:

• Scaling our patient data consolidation pipeline (deduplication, normalization, hydration) to handle 100x today's volume without 100x the cost.

• Building pipelines that deliver clinical data directly into customers' data warehouses, reliably and at scale.

• Building the ingestion path for customers pushing large volumes of their own data into the platform.

• Building document-processing pipelines that extract structured data from PDFs, images, and free text to feed ML models.

• 6+ years of engineering experience, with a heavy lean towards data engineering — building, maintaining, and scaling pipelines processing terabytes of data and millions of events a day.

• Experience across the data stack — ingestion, storage, processing, warehousing, serving — and an understanding of the tradeoffs (cost, latency,…