5-7 years of data engineering experience, preferably in healthcare or population health analytics.
Mastery of Epic systems, including Registries, Chronicles, Clarity, and Caboodle.
Proficiency in Snowflake for data warehousing and DBT for data transformation and modeling.
Strong background in healthcare claims processing, both medical and pharmacy.
Familiar with Milliman MedInsight data models for analytics workflows.
Expertise in SQL and data modeling processes.
Experience in building and optimizing data pipelines.
Responsibilities
Design and maintain scalable data pipelines for population health and claims analytics.
Work with Epic data sources to extract and integrate relevant healthcare data.
Create data models supporting advanced analytics for quality measures and care management.
Operationalize risk scoring data models and analytics for healthcare.
Transform healthcare claims data for effective analytics and reporting.
Utilize Milliman MedInsight structures for payer-provider analytics.
Collaborate with cross-functional teams to translate business needs into data solutions.
Benefits
Opportunity to work with cutting-edge healthcare analytics technologies.
Collaborative work environment that values interdisciplinary teamwork.
Engagement with meaningful projects impacting population health and patient outcomes.
Potential for career growth within a healthcare-focused organization.
Full Job Description
We are seeking a skilled Population Health Data Engineer with deep expertise in Epic data ecosystems and healthcare analytics. This role will focus on designing, building, and optimizing data pipelines and models to support population health, quality of care and claims analytics.
Key Responsibilities
Design, develop, and maintain scalable data pipelines supporting population health, claims analytics, and reporting.
Work extensively with Epic data sources including Registries, Rosters, Chronicles, Clarity, and Caboodle.
Integrate clinical and claims data to support longitudinal patient views and advanced analytics.
Develop data models for population health use cases including quality measures, risk stratification, utilization, and care management analysis.
Support development and operationalization of risk scoring data models and analytics (e.g., MARA, HCC, RAF).
Process and transform healthcare claims data (medical and pharmacy) for analytics and reporting.
Work with Milliman MedInsight data structures to support payer-provider analytics and efficiency benchmarking.
Build and optimize ELT pipelines using modern cloud platforms.
Collaborate with healthy planet, efficiency, quality, clinical, and analytics teams to translate business needs into technical solutions.
Ensure data quality, governance, and compliance with healthcare regulations (e.g., HIPAA).
Optimize performance of large-scale datasets and queries.
Required Qualifications
Strong hands-on experience with Epic systems, including:
Epic Registries
Chronicles data structures
Hyperspace or Hyperdrive environments
Clarity and Caboodle data models
Experience with modern data engineering tools and platforms:
Snowflake (data warehousing)
DBT (data transformation and modeling)
Dynamic Tables in Snowflake
Solid understanding of healthcare domain concepts, including population health and value-based care.
Experience with healthcare claims processing (medical and pharmacy claims).
Hands-on experience with Milliman MedInsight data models and analytics workflows.
Strong SQL and data modeling expertise.
Experience building and maintaining data pipelines.
Key Skills
Population Health & Risk Analytics
Healthcare Data Modeling (Clinical and Claims)
Epic Data Ecosystem Expertise
Snowflake & DBT
SQL & Performance Optimization
Data Governance & Compliance
Education & Experience
Bachelor's or master's degree in computer science, Health Informatics, Data Engineering, or related field.
6+ years of experience in data engineering, with strong preference for healthcare, payer, or population health analytics experience.
