Founding Analytics Engineer

Building the financial operating system for healthcare, and bringing the joy back to healthcare by fixing the financial chaos behind it.

The healthcare payment system is a complex and inefficient maze - healthcare practices leave $125 billion in revenue uncollected each year, lost in the chaos of fragmented financial data, manual workflows, and opaque payer systems. This financial uncertainty leaves practices struggling to stay afloat, while valuable revenue slips through the cracks.

Joyful Health is building the AI-powered financial operating system for healthcare practices. Our mission is to bring the joy back to running a private practice by simplifying financial operations so providers can focus on patient care. We spent 10 months working as fractional CFOs for a dozen practices, doing this work side by side with providers as we developed our product.

We just closed a funding round led by world-class investors led by CRV and XYZ and angels including the founders of Mongo

DB & KAYAK.

This role is full-time. We’re looking for someone NYC-based who is open to coming into the office 3 days a week.

The base pay range for this role is $150,000–$275,000 per year.

We're looking for a talented and ambitious founding analytics engineer with 5+ years of experience in building data models and analytics infrastructure who's excited to create the analytics foundation from the ground up. You'll be the first analytics hire, responsible for designing and implementing the entire analytics infrastructure, defining our metrics framework, and transforming complex healthcare financial data into actionable insights.

Healthcare financial data is notoriously complex and fragmented - each practice has unique systems, inconsistent data structures, and limited visibility into their financial performance. This has meant that very few solutions have emerged in the market that actually help practices make informed decisions about their business. We've begun to crack this challenge, and now we need someone to build the analytics infrastructure and system design that will enable both our team and our customers to make data-driven decisions at scale.

As the founding analytics engineer, you'll have an outsized impact on the company's trajectory, establishing the analytics patterns and practices that will scale with us. This is a rare opportunity to build something from scratch - you'll own the entire analytics stack, from infrastructure and data modeling to visualization and metrics definitions. You should be comfortable (and excited!) about working in a fast-paced, early-stage startup environment where you'll be able to wear many hats and take on new challenges as they arise.

This role is full-time. We're looking for candidates based in NYC.

• Build the analytics infrastructure from scratch - design and implement the entire analytics stack, including the data warehouse architecture, transformation layer, semantic layer, and visualization tools.

• Establish foundational data models - create robust, scalable data models that transform raw healthcare financial data into clear, actionable metrics and insights. Define the modeling patterns and conventions that the team will follow as we scale.

• Design the metrics framework - work with leadership to define key business metrics, establish metrics definitions, and build the infrastructure to track them reliably. Own how we measure success across the organization.

• Create self-service analytics capabilities - build intuitive dashboards and analytics tools that enable both internal teams and customers to understand revenue cycle performance, identify uncollected revenue, and track financial health.

• Drive the analytics roadmap - partner closely with product, engineering, and customer success to prioritize analytics initiatives, define requirements, and ensure we're building the right solutions.

• Build data transformation pipelines - implement modern ELT/ETL patterns using tools like dbt to ensure data quality, reliability, and performance at scale.

• Establish best practices and standards - define data governance policies, documentation standards, testing frameworks, and…