Develop Health is on a mission to use AI to radically accelerate access to life-saving medications. By automating complex, manual healthcare processes—like benefit verification and prior authorization— we've been growing rapidly and over the past couple of years have helped over 3.6 million patients!

We’re partnering with some of the largest pharmacy benefit managers and payers in the nation, revolutionizing how healthcare interactions occur by eliminating human delays and inefficiencies. Our small, elite team of founders and engineers have previously launched and exited successful healthcare startups including Rupa Health and Canvas Medical. We are now scaling rapidly following a major funding round.

About the Role

We're hiring a Lead Analytics Engineer to own analytics at Develop Health. You'll reinvent how an AI-first company uses data.

Our platform sits inside the workflows that determine how quickly patients get medication. You'll own and evolve our existing customer-facing analytics product, used by providers, pharmacies, and healthcare organizations to understand approval outcomes, turnaround times, and operational bottlenecks, then decide where to intervene. The same data will shape company strategy and help our internal teams focus on the highest-impact opportunities.

You'll also build an AI-native analytics system: trusted semantic models, reusable skills, and analytics agents that can answer common questions and investigate changes safely. The goal is to multiply your impact so teams can get reliable answers without routing every request through one person.

This is a hands-on individual-contributor role with ownership of the roadmap, data platform, stakeholder relationships, internal insights, and customer-facing analytics. You'll work from our Menlo Park office at least three days per week.

What You'll Do — Impact in Your First 3–6 Months

Work directly with Product, Operations, Engineering, Finance, and Go-to-Market leaders to find the decisions where better data can change the outcome, then own those problems through adoption.

Strengthen our dbt and Snowflake foundation and establish trusted metrics for patient access, automation, operational performance, and customer outcomes.

Advance our customer-facing analytics product so customers can understand performance, identify bottlenecks, and take action to help patients get medication faster.

Build a set of governed analytics agents, reusable skills, and self-service workflows that turn repeated requests into reliable capabilities available across the company.

What You'll Own — Beyond 6 Months

The analytics roadmap and the operating model for discovering, prioritizing, and delivering the company's highest-value data work.

A shared semantic and metrics layer that keeps internal decisions, customer reporting, and AI-generated analysis consistent.

An AI-native, self-service analytics platform that gives a lean analytics function disproportionate reach: internal teams can answer routine questions and investigate changes without sacrificing accuracy, context, or trust.

The architecture and standards that let our data products scale with the company, plus technical mentorship for future analytics hires.

What You'll Bring on Day One

Analytics engineering craft. You have at least 3 years of hands-on analytics or data engineering experience, with deep expertise in SQL and dbt and experience with Snowflake or another modern cloud data warehouse.

Problem ownership. You work directly with stakeholders to turn ambiguous questions into the right analysis, metric, model, dashboard, workflow, or data product. You carry it through adoption and measurable impact.

Clear communication. You surface assumptions, explain trade-offs, and make complex data useful to technical and non-technical partners.

AI-native execution. You're excited to build with agents and emerging analytics tools while owning the definitions, validation, and guardrails that make their answers trustworthy.

What You'll Need to Learn Quickly

How benefit verification and prior authorization work across providers, pharmacies, payers, and patients.