Lead Analytics Engineer - Medicaid

We’re partnering with a fast-scaling healthcare technology company building modern infrastructure to improve healthcare delivery and patient outcomes at scale.This role supports a major national health plan as it expands into a new Medicaid business line, building on an established Medicare Advantage analytics practice. You’ll own the Medicaid analytical build-out end-to-end - including the data model, pipeline, and visualization - while supporting and extending the broader reporting suite.Working closely with the analytics team lead, you’ll be the day-to-day expert for the Medicaid workstream, understanding customer needs, prioritizing work, and communicating directly with stakeholders, driving better decisions and operational outcomes across healthcare products and services.What's on offerTotal compensation up to $185kRemote-first environment with strong flexibilityOpportunity to own and shape a critical data domain at scaleHigh-impact role within a mission-driven healthcare technology businessCollaborative engineering culture investing in modern data platforms and engineering practicesWhat You'll Need To Have Experience with dbt or similar transformation toolingStrong SQL skills, comfortable working within complex, established data models Hands-on QuickSight or comparable BI experience, across both data and visualization Healthcare data experience, ideally claims/clinical data within Medicare Advantage or MedicaidStrong customer-facing and stakeholder management skills, with the confidence to own a key workstreamExperience building new reporting or data solutions within an established analytics environmentOrganized, proactive, and comfortable managing priorities independentlyBonusExperience in healthcare or regulated environmentsExposure to large-scale operational or clinical datasetsExperience with modern cloud data platforms (Snowflake, BigQuery, Databricks, etc.)Background in platform thinking, reusable frameworks, or self-serve analytics enablementExperience introducing data reliability or observability practices at scaleSounds interesting? Please apply to find out more!