Founding Analytics Engineer

Joyful Health•New York City, NY

2d•Hybrid

About The Position

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 announced our $22M Series A led by CRV and world-class investors including the founders of MongoDB & 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 $120,000–$235,000 per year.

Requirements

5+ years of experience in analytics engineering, data analytics, or business intelligence, with a focus on building data models and metrics frameworks.
SQL and data modeling best practices, with the ability to write efficient, maintainable queries
Modern analytics tools such as dbt, Looker, Tableau, or similar platforms
Python for data analysis and automation
Data warehousing concepts and cloud data platforms (Snowflake, BigQuery, Redshift, etc.)
Version control and CI/CD workflows such as Github actions

Nice To Haves

Experience working with healthcare data or financial analytics is a plus.
You naturally empathize with users and enjoy designing solutions with their needs in mind.
You're excited to shape the future of our analytics capabilities and are excited to lead by example – as an early team member, you'll have the chance to influence everything from our data modeling approach to our analytics culture.
You have a radical sense of ownership with the ability and desire to own the analytics roadmap and refine ambiguous problems.
You also have a low ego, growth mindset, and desire to see the product and team scale with your impact.
You have a love for your craft and strong technical execution - you view analytics engineering as a craft, not just a task, and you're excited about using data to create meaningful impact.
Your technical skills go beyond writing SQL—you care about building analytics that drive real business decisions, with the user at the forefront.
We care more about your drive, hustle, and passion than a college degree from a shiny institution or experience at name-brand companies.

Responsibilities

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 code review processes that will serve as the foundation for the analytics team as it grows.
Define business logic - collaborate on complex revenue recognition rules, financial calculations, and healthcare billing logic that powers our analytics.