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Kairon Health is a healthcare software company that builds analytics platforms for large healthcare organizations and physician networks. The platform identifies care delivery breakdowns, recommends interventions, tracks operational responses, and measures outcomes using rigorous methodology aligned with how government programs and insurers calculate performance.
As Staff Data Analyst, you will own the analytical engine behind the product—not a support or reporting role, but a founding-team position responsible for building the core measurement systems that customers rely on for operational and financial decisions.
Key responsibilities include:
- Own healthcare performance metric methodology across payer, EHR, claims, clinical, operational, and government-program data sources
- Design, build, and validate SQL-based analytical logic that becomes part of the core product
- Transform messy, unreliable healthcare data into trusted measurement systems that withstand scrutiny from executives, clinicians, payers, and government stakeholders
- Build validation frameworks to detect source-system issues, feed changes, missing data, and metric drift
- Develop quasi-experimental approaches (before/after, cohort, tenure-controlled) to measure intervention impact
- Partner with product and engineering to embed analytics into operational workflows rather than standalone reports
- Work with customer-facing teams to understand how healthcare leaders interpret and act on data
- Analyze qualitative workflow information alongside quantitative data to explain not only what is happening, but why
- Help define analytical standards as the company scales
- Communicate findings clearly to internal and external stakeholders, including non-technical healthcare leaders
The technical stack includes Snowflake (SQL-as-Code), Python ETL on AWS, PostgreSQL, and a composable metric engine built from reusable primitives. You'll work in a SQL-first, version-controlled, productized analytics environment—not notebooks and dashboards.
Within 6–12 months, success means improving trustworthiness and scalability of core metrics, identifying and resolving data-quality issues before they affect customers, building analytical approaches that demonstrate intervention impact, and becoming the go-to owner for hard questions about healthcare measurement and data validity.
The company is small (fewer than 15 people), with everyone close to customers, product, data, and decisions. You'll report directly to the CEO and work closely with product, engineering, and customer-facing teams.
