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Caboodle Data Engineer Jobs (NOW HIRING)

Lorven Technologies

Data Engineer Epic ETL

Data Engineer Epic ETL Full Time Remote The Data Engineer is responsible for delivering data ... such as Clarity, Caboodle, and Cogito to enable enterprise reporting and analytics. Key ...

Interwell Health

Staff Data Engineer

Reporting to the Director of Data Engineering, the Staff Data Engineer serves as a senior technical ... Experienced in implementing and supporting Epic integrations, leveraging Cogito Cloud and Caboodle ...

Apex Systems

Population Health Data Engineer

Boston, MA · Remote

$80 - $85/hr

Job#: 3033213 Population Health Data Engineer Location: Remote, with occasional travel to Boston ... and Caboodle. * Integrate clinical and claims data to support longitudinal patient views and ...

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How much do caboodle data engineer jobs pay per year?

As of Jun 7, 2026, the average yearly pay for caboodle data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What are Caboodle Data Engineers?

Caboodle Data Engineers are IT professionals who specialize in managing, developing, and optimizing the Epic Caboodle data warehouse platform, which is commonly used in healthcare organizations. They create and maintain data pipelines, integrate diverse healthcare data sources, and ensure data quality and integrity for analytics and reporting. Their work supports clinical, operational, and financial decision-making by making accurate and reliable data accessible to stakeholders.

How does a Caboodle Data Engineer typically collaborate with clinical and IT teams in a healthcare setting?

Caboodle Data Engineers work closely with clinical staff and IT professionals to ensure that data pipelines and integrations meet the needs of healthcare providers. They often participate in cross-functional meetings to understand reporting requirements, troubleshoot data issues, and optimize workflows within the Epic Caboodle data warehouse environment. Effective communication is key, as they must translate technical database concepts into actionable insights for non-technical stakeholders. This collaborative approach helps align data infrastructure with real-world clinical operations and regulatory compliance.

What are the key skills and qualifications needed to thrive as a Caboodle Data Engineer, and why are they important?

To thrive as a Caboodle Data Engineer, you need expertise in data modeling, SQL, ETL processes, and a solid understanding of healthcare data standards, typically supported by a degree in computer science or a related field. Familiarity with Epic Caboodle data warehouse, Clarity, reporting tools like SAP BusinessObjects, and certifications such as Epic Caboodle Data Model certification are highly valued. Strong problem-solving, attention to detail, and effective communication skills help you collaborate with stakeholders and interpret complex data requirements. These abilities are crucial for ensuring accurate, compliant, and actionable healthcare analytics that support clinical and operational decision-making.
Infographic showing various Caboodle Data Engineer job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Data Architect [ Must Have EPIC Caboodle $ Population Health ]

STI

Remote

$65.25 - $84/hr

Full-time

Posted 18 days ago

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.

Apply