Data Engineer, Healthcare

Percepta • New York, NY

20h • $180,000 - $250,000

About The Position

We're hiring one of the founding members of Percepta's data team — a role that lives across the full spectrum from data engineering to data science to ML engineering. You won't be boxed into one of those; the best person here has a center of gravity in one and real range across the others.
The job has two halves, and you'll do both: Be the data person. Build the pipelines, models, analysis, "data packs," and ontology that turn messy health-system data into something both AI and humans can actually use — and do it fast, inside real customer environments. Build the product around that. Build the tooling, abstractions, and increasingly agentic/automated systems that make the first half faster and compounding across every health system we work with. This is where you set the taste and help form our strategy for how Percepta does data — not as a one-off, but as something that gets better every time we do it. As a founding hire, you're not inheriting a playbook — you're writing it.

Requirements

• You can build in ambiguity, you form opinions and ship, and you care about building leverage, not just outputs.
• Strong experience around some combination of Data Science, Data Engineering, Machine Learning.
• A product instinct for the second half of the job — you want to build the thing that makes the work easier, not just do the work
• Health-system data experience is required for this role: you've worked hands-on with EHR data (e.g., Epic), and/or claims data, and other operational healthcare datasets (ADT/scheduling, SDOH, payer data)
• High ownership and strong communication — you're comfortable embedded directly with customer teams

Nice To Haves

• Experience building agentic or automated data-engineering tooling
• Hands-on experience with modern cloud data platforms (e.g., Databricks)
• Prior startup, founding, or forward-deployed experience

Responsibilities

• Build end-to-end pipelines and models that turn fragmented clinical, operational, and financial data into high-leverage, AI-ready assets
• Structure and normalize noisy datasets — defining the data packs and ontology that our AI engineers build on top of
• Build the internal product and tooling that makes data work faster and repeatable across health systems, so each engagement compounds rather than starts from zero
• Work directly with clinicians, operators, and product/AI engineers to turn high-value use cases into production data workflows
• Form strong technical opinions on data models, storage, orchestration, and infra tradeoffs — and make the calls

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed