Director, Product Data Engineering

Qualified Health•Palo Alto, CA

1d•$195,000 - $240,000•Hybrid

About The Position

Transform healthcare with us. At Qualified Health, we're redefining what's possible with Generative AI in healthcare. Our infrastructure provides the guardrails for safe AI governance, healthcare-specific agent creation, and real-time algorithm monitoring — working alongside leading health systems to drive real change. This is more than just a job. It's an opportunity to build the future of AI in healthcare, solve complex challenges, and make a lasting impact on patient care. If you're ambitious, innovative, and ready to move fast, we'd love to have you on board. Join us in shaping the future of healthcare. Job Summary: The Director of Product Data Engineering is one of the most important leadership hires we'll make this year. You'll build and lead the team responsible for every reusable data product artifact at Qualified Health — transformation notebooks, validation pipelines, data specifications, IaC modules, and the initial agentic LLM call development that sits at the heart of our clinical AI workflows. Today, this work is being done ad-hoc by our integration team alongside customer deployments. You're going to change that. You'll create a dedicated product engineering function that builds tested, documented, reusable artifacts — the kind that can be deployed to a new health system partner in days instead of weeks. You own the backlog, you set the technical standards, and you drive the architecture decisions that determine how our data products scale. This is a ground-floor opportunity to shape how Qualified Health builds its core data product layer. You'll work across three product families — revenue cycle, care gap optimization, and quality registry abstraction — each with multiple active agentic workflows. The right person in this role will dramatically increase the throughput of our integration teams by eliminating per-partner reinvention and replacing it with a library of production-grade, reusable components.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field
8+ years in data engineering, with 3+ years in a technical leadership role
Deep Databricks/Spark expertise at the architecture level — performance tuning, cluster management, Delta Lake design patterns
Experience building reusable data frameworks, tooling, or platforms
Demonstrated ability to hire, develop, and retain strong engineers
Ability to travel for team onsites, leadership meetings or partner-facing architecture discussions 5-10% of the time

Nice To Haves

Exposure to ML/LLM pipeline development — model serving, prompt engineering in production, evaluation frameworks
Healthcare data experience (Epic Clarity, clinical data models, FHIR)
IaC experience (Terraform, Bicep)
Background building product-oriented data platforms (not just ETL/ELT pipelines)
Prior experience at a growth-stage startup where you built a team from scratch
Architectural Vision: Ability to see across multiple product families and design data foundations that serve all of them — not one-off solutions for each
Product Engineering Instincts: You think about your output as a product: versioned, tested, documented, and designed for the consumer (in this case, the integration team deploying it to partners)
Technical Depth + Leadership Range: You're comfortable operating at both the architecture whiteboard and the code review level — and you know when to toggle between them
Influence Without Authority: You'll need to drive adoption of your team's artifacts across integration pods that have their own priorities and habits
Builder Mentality: You're energized by creating something from nothing — building a team, establishing processes, and shipping the first version of things that don't exist yet

Responsibilities

Build and lead a team of 4-5 data engineers focused on reusable product artifacts
Own the product data engineering backlog in partnership with product management
Define and enforce technical standards for notebooks, pipelines, QC modules, and documentation
Drive the development of reusable data provisioning modules (IaC) and pipeline tooling
Lead the build-out of data transformation and validation notebooks for agentic workflows
Oversee initial agentic LLM call development within data pipelines — including prompt engineering, model serving patterns, and evaluation frameworks
Own data spec documentation and ensure specs are maintained as products evolve
Collaborate closely with Client Integration Directors to ensure product artifacts deploy cleanly into customer environments
Develop team members technically and professionally; create a high-output engineering culture