_{22nd August, 2026}

Role: Senior Synthetic Data Engineer.
Location: 100% Remote.
 
Job Description:
The Senior Synthetic Data Engineer is responsible for designing, developing, and maintaining the enterprise Synthetic Data Platform supporting the Community Care Network Next Generation (CCN NG) program.
 
This role serves as the technical lead for synthetic healthcare transaction generation, creating reusable, standards-conformant datasets that enable integration validation, software development, demonstrations, training, and operational readiness without reliance on sensitive production data.
 
Working within the Synthetic Data & Automation Engineering Team, the Senior Synthetic Data Engineer develops scalable data generation capabilities that model real-world healthcare business scenarios while ensuring compliance with healthcare interoperability standards and program requirements.

This role transforms complex business rules into reusable synthetic data assets that become foundational components of the program's engineering platform.
 
Reporting to the Automation & Synthetic Data Architect, this individual collaborates closely with Healthcare Interoperability Engineers, Automation Intelligence Engineers, Senior Automation Engineers, and Integration Validation Engineers to deliver governed, reusable synthetic data assets supporting the CCN NG product portfolio.
 
Description Mission:
Design, build, and continuously evolve the CCN NG Synthetic Data Platform by developing reusable, standards-conformant healthcare transaction datasets and data generation capabilities that accelerate engineering, integration validation, and product delivery while eliminating dependency on production data.
 
Job Responsibilities:
 
Synthetic Data Platform Engineering

• Design, develop, and maintain the enterprise Synthetic Data Platform supporting healthcare transaction processing.
• Develop scalable data generation services capable of producing standards-conformant healthcare transaction datasets.
• Create reusable synthetic member, provider, eligibility, claim, authorization, and payment scenarios representing real-world business conditions.
• Ensure generated datasets remain PHI-free while accurately modeling operational healthcare workflows.

Data Engineering

• Design data models supporting complex healthcare relationships and transaction lifecycles.
• Develop automated data generation pipelines and supporting APIs.
• Implement versioning, traceability, and lifecycle management for synthetic datasets.
• Optimize data generation performance, scalability, and maintainability.

Platform Asset Development

• Establish and maintain the program's master synthetic healthcare transaction library.
• Develop reusable reference datasets supporting engineering, integration validation, demonstrations, and training.
• Create parameterized data generation capabilities supporting rapid creation of new healthcare scenarios.
• Maintain engineering documentation supporting synthetic data assets and generation processes.

Standards Compliance

• Collaborate with Healthcare Interoperability Engineers to ensure generated data complies with ANSI X12 implementation guides and companion guides.
• Validate generated datasets against healthcare transaction standards and business rules.
• Support rapid implementation of evolving interoperability requirements and transaction changes.

Engineering Collaboration

• Partner with Automation Intelligence Engineers to integrate synthetic data generation into intelligent engineering workflows.
• Support Senior Automation Engineers by providing reusable datasets for automated integration validation.
• Collaborate with Integration Validation Engineers to validate accuracy, completeness, and consistency of generated datasets.
• Contribute to continuous improvement of the Synthetic Data & Automation Platform.

Innovation & Continuous Improvement

• Evaluate emerging synthetic data technologies and engineering practices.
• Recommend improvements to data generation methodologies and platform capabilities.
• Promote reuse, automation, and standardization across engineering initiatives.

Contribute to the development of engineering best practices supporting enterprise synthetic data management.
 
Required Skills:

• Experience designing and developing enterprise data models and data generation solutions.
• Strong software engineering experience using Python or comparable programming languages.
• Experience developing APIs, automation services, or distributed applications.
• Experience working with relational databases, data modeling, and data transformation techniques.
• Strong understanding of software engineering principles, object-oriented design, and modern development practices.
• Excellent analytical, problem-solving, and collaboration skills.

Desired Qualifications:

• Experience developing synthetic data generation capabilities.
• Experience supporting healthcare payer, provider, or federal healthcare programs.
• Familiarity with ANSI X12 healthcare transaction standards.
• Experience with graph databases or relationship-based data modeling.
• Experience building cloud-native applications and containerized services.
• Experience implementing CI/CD pipelines and DevSecOps practices.
• Experience with AI-assisted software development tools such as Cursor, GitHub Copilot, or similar engineering platforms.
• Experience working within Agile software development environments.

Desired Technical Skills

• Python
• Data engineering and software development
• Synthetic data generation methodologies
• RESTful API development
• SQL and relational databases
• Data modeling and schema design
• Cloud-native application development
• Containerization technologies
• Git-based source control
• CI/CD pipelines
• Agile software engineering

Education and Experience:

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Data Engineering, Information Systems, or a related technical discipline, or equivalent professional experience.
• Ten (10) or more years of experience, including seven (7) or more years of experience in software engineering, data engineering, or enterprise application development.

Success Measures (First 12-18 Months)
The Senior Synthetic Data Engineer will be expected to:

• Design and deliver the CCN NG Synthetic Data Platform supporting enterprise engineering activities.
• Develop a governed master library of reusable synthetic healthcare transaction datasets covering prioritized business scenarios.
• Implement scalable data generation capabilities enabling rapid creation of new transaction scenarios in response to changing requirements and interoperability standards.
• Eliminate dependency on production data for integration validation through delivery of high-quality, standards-conformant synthetic datasets.
• Establish reusable APIs and engineering services supporting synthetic data generation across the CCN NG product portfolio.
• Collaborate with the Synthetic Data & Automation Engineering Team to ensure synthetic datasets seamlessly support intelligent automation workflows and automated integration validation capabilities.
• Continuously improve the performance, quality, scalability, and maintainability of the Synthetic Data Platform as a strategic engineering asset.

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