Genzeon, an AI and automation company with deep engineering and data expertise, dedicated to serving the healthcare and retail industries. Our platform solutions – including HIP One, CompliancePro Solutions, and Patient Engagement Solutions – empower organizations to scale innovation and transform outcomes.
Genzeon is a global community of innovators and problem-solvers, with a culture built on inclusion, flexibility, and purpose-driven work. With four global delivery centers, we support providers, payers, Healthtech, and retail organizations worldwide.
Genzeon has an exciting opening for Data Engineer – MMIS (Medicaid Management Information System) to join our dynamic team.
Job Title: Data Engineer – MMIS (Medicaid Management Information System)
Location: Remote (USA)
Job Type: Contract
Role Summary
We are seeking an experienced Data Engineer with MMIS (Medicaid Management Information System) experience to design, build, and support scalable data pipelines and analytics platforms for large Medicaid healthcare programs. This role will support client’s MMIS modernization and analytics initiatives by enabling reliable ingestion, transformation, and delivery of Medicaid data for downstream analytics, reporting, and data science use cases.
The ideal candidate will have strong hands‑on experience with Medicaid claims and encounter data, advanced SQL and Python, and a deep understanding of MMIS data structures, CMS compliance needs, and payer systems.
Key Responsibilities
- Design, build, and maintain scalable data pipelines for MMIS and Medicaid data sources
- Ingest, process, and transform large‑scale healthcare datasets including claims, encounters, provider, and member data
- Collaborate with Data Scientists, BI teams, and program stakeholders to deliver analytics‑ready datasets
- Support predictive modeling, fraud/waste/abuse (FWA) analytics, and cost optimization initiatives through high‑quality data engineering
- Perform data validation, cleansing, and quality checks aligned with CMS and state Medicaid requirements
- Optimize SQL queries and data processing workflows for performance and reliability
- Support federal and state reporting needs by preparing compliant, governed datasets
- Ensure data security, governance, and HIPAA compliance standards are met
- Troubleshoot pipeline failures and support production environments
- Translate business and reporting requirements into scalable data engineering solutions
Required Qualifications
- Bachelor’s or Master’s degree in:
- Computer Science
- Data Engineering / Data Science
- Healthcare Informatics
- Statistics / Mathematics or related field
- 6+ years of experience in Data Engineering or Analytics Engineering roles
- Hands‑on MMIS experience (MANDATORY)
- Experience working with Medicaid or Healthcare Payer systems
- Strong proficiency in:
- SQL / PL‑SQL (advanced querying)
- Python (for data processing and pipeline development)
- Experience handling large, complex healthcare datasets
- Experience supporting compliance‑driven environments (CMS, HIPAA)
Technical Skills
Data Engineering & Platforms
- Data pipeline development and optimization
- Data ingestion, transformation, and validation
- Data governance and quality management
Tools & Technologies
- Python (Pandas, NumPy)
- SQL / PL‑SQL
- SAS (preferred in Medicaid environments)
- Hadoop / Spark (nice to have)
- Tableau, Power BI, or similar BI tools
- Cloud platforms: AWS, Azure, or Google Cloud Platform
Healthcare & MMIS Knowledge
- MMIS platforms and Medicaid data models
- Medicaid claims processing workflows
- Encounter data, provider data, member eligibility data
- CMS reporting standards and compliance
- Fraud, Waste & Abuse (FWA) analytics support
Preferred Qualifications
- Experience working on MMIS modernization projects
- Familiarity with healthcare data standards:
- HIPAA
- ICD‑10
- CPT
- HL7 / FHIR (nice to have)
- Experience supporting Data Science or Machine Learning teams
- Experience working in Agile / Scrum environments
- Strong communication skills and ability to collaborate with cross‑functional teams
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