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 offer
Total compensation up to $185k
Remote-first environment with strong flexibility
Opportunity to own and shape a critical data domain at scale
High-impact role within a mission-driven healthcare technology business
Collaborative engineering culture investing in modern data platforms and engineering practices
What You'll Need To Have
Experience with dbt or similar transformation tooling
Strong 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 Medicaid
Strong customer-facing and stakeholder management skills, with the confidence to own a key workstream
Experience building new reporting or data solutions within an established analytics environment
Organized, proactive, and comfortable managing priorities independently
Bonus
Experience in healthcare or regulated environments
Exposure to large-scale operational or clinical datasets
Experience with modern cloud data platforms (Snowflake, BigQuery, Databricks, etc.)
Background in platform thinking, reusable frameworks, or self-serve analytics enablement
Experience introducing data reliability or observability practices at scale
Sounds interesting? Please apply to find out more!

Work arrangement
Yes