Position Overview

Kairon Health is hiring a Staff Data Analyst, Healthcare to build and own the analytical engine behind our product.

This is not a support analyst role. It is not a report-building role. The work you do will become part of the product itself: metric methodology, data validation, outcome measurement, causal analysis, and the analytical logic that customers rely on to make operational and financial decisions.

You will report directly to the CEO and work closely with product, engineering, customer-facing teams, and healthcare operators. You will be responsible for turning complex, unreliable healthcare data into trusted measurement systems that can stand up to scrutiny from executives, clinicians, payers, and government-program stakeholders.

This is a senior individual-contributor role. You won't manage people (at least initially) - you'll own the analytical engine itself.

Key Responsibilities

• Own the methodology for healthcare performance metrics across payer, EHR, claims, clinical, operational, and government-program data sources.
• Design, build, and validate SQL-based analytical logic that becomes part of Kairon's core product.
• Translate messy healthcare data into reliable measures of care gaps, intervention eligibility, performance, financial opportunity, and realized outcomes.
• Build validation frameworks to detect source-system issues, feed changes, missing data, contradictory data, and metric drift.
• Develop before/after, cohort, tenure-controlled, and other quasi-experimental approaches to measure whether interventions actually worked.
• Partner with product and engineering to embed analytics into operational workflows rather than standalone reports.
• Work with customer-facing teams to understand how physician practices, care teams, and healthcare executives interpret and act on data.
• Analyze qualitative workflow and sentiment information alongside quantitative data to explain not only what is happening, but why.
• Help define the analytical standards Kairon uses across customers as the company scales.
• Communicate findings clearly to internal and external stakeholders, including non-technical healthcare leaders.

Our Stack

• Snowflake as the analytics warehouse - version-controlled SQL routines ("SQL-as-Code") producing our metric layer
• A composable metric engine: metrics built from reusable population, numerator/denominator, and data-attribute primitives rather than one-off queries
• Python ETL on AWS (ECS Fargate, Step Functions) ingesting claims (CCLF/BCDA), CMS attribution reports, EHR/clinical, ADT, labs, and private-payer feeds
• PostgreSQL for application data, replicated to Snowflake via CDC
• Django/DRF backend, Vue 3 frontend (analytics ship inside the product)

You don't need every piece of this on day one, but you should be excited to work in SQL-first, version-controlled, productized analytics - not notebooks and dashboards.

What Success Looks Like

Within your first 6-12 months, you will have:

• Improved the trustworthiness, documentation, and scalability of core metric methodology.
• Identified and resolved meaningful data-quality or measurement issues before they affected customer decisions.
• Built analytical approaches that help customers understand the impact of their interventions, not just their activity levels.
• Contributed directly to product capabilities that make Kairon more differentiated and defensible.
• Become a go-to owner for hard questions about healthcare measurement, attribution, metric logic, and data validity.

What We're Looking For

• 5+ years experience as a data analyst, analytics engineer, data scientist, healthcare analyst, or similar role working with complex real-world data.
• Strong SQL skills, including comfort working with layered transformations, messy source systems, and production analytical logic.
• Experience with healthcare data such as claims, EHR data, payer feeds, Medicare programs, quality measures, value-based care, risk adjustment, care gaps, utilization, or population health.
• Strong judgment about data quality, metric design, measurement bias, denominator/numerator logic, and edge cases.
• Ability to reason rigorously about causality, attribution, before/after comparisons, cohort construction, and confounding factors.
• Comfort operating in ambiguity without waiting for perfectly clean requirements.
• Ability to communicate analytical decisions clearly to both technical and non-technical audiences.
• Interest in building productized analytics, not one-off reports.
• High ownership orientation and comfort working on a small team with direct executive visibility.

Especially Valuable

• Experience with value-based care, accountable care organizations, Medicare Advantage, MSSP, HEDIS, Stars, quality programs, or payer/provider performance measurement.
• Experience building data validation, reconciliation, or observability systems for healthcare data.
• Familiarity with dbt or similar transformation frameworks.
• Experience analyzing intervention impact in operational healthcare settings.
• Ability to work across both quantitative datasets and qualitative operational context.
• Prior experience in an early-stage or small-company environment.

What This Role Is Not

This is not a role for someone who wants to primarily build dashboards from clean datasets. It is not a ticket-taking analytics support role. It is not limited to answering ad hoc questions after the product is built.

This role is for someone who wants to define how healthcare performance should be measured, build the logic that makes that measurement reliable, and see that work directly shape a product used by healthcare organizations responsible for large patient populations.