Lead - Biomedical Data Factory Team

The Lead of the Clinical Data Factory supports the data ecosystem that powers MUSC’s AI development workflows. This role ensures PI/PHI‑compliant synthetic data pipelines, model‑training readiness, system stability, and data availability by working w/IS architecture to keep the mini-arch ahead of major changes for continued workflow. The Lead works closely with Central IS Architecture and Security, and the AI Center to maintain an emergent AI architecture that reduces load on central IT while enabling rapid experimentation and compliant model development.

Entity

University Medical Associates (UMA) Only Employees and Financials

Worker Type

Employee

Worker Sub-Type

Classified

Cost Center

CC005532 EVPAA - Center for Artificial Intelligence

Pay Rate Type

Salary

Pay Grade

Health-34

Scheduled Weekly Hours

40

Work Shift

Job Description

Primary Areas of Responsibility (with % Allocation)

• Synthetic Data Pipeline Management & Compliance – 40%
• Maintain high‑performance data pipelines meeting PI/PHI compliance standards.
• Audit, monitor, and improve data quality, performance, and lineage.
• Support model‑training workflows with high‑integrity synthetic datasets.
• Ensure operational compliance, and follow through with IS Strategies and Approaches
• System Architecture, Maintenance & Enhancement – 25%
• Oversee lifecycle maintenance of AI Center Built Tools, patching, and upgrades to mini data infrastructure.
• Partner with enterprise architecture teams to ensure alignment with evolving systems.
• Communicate risks, dependencies, and required enhancements proactively.
• Team Leadership & Technical Guidance – 20%
• Lead and mentor Jr. Data Engineers and Jr. Architects.
• Maintain documentation, operational workflows, and technical standards.
• Coordinate team activities to meet service and uptime commitments.
• Integration with AI Project Teams – 10%
• Collaborate with AI Strategy & Ops, Research and AI Incubation teams to ensure data needs are met.
• Provide technical support for data provisioning, synthetic layering, and model experimentation for the AI Center activities.
• Governance, Quality & Model Integrity Support – 5%
• Maintain data governance practices that support ethical model development.
• Support model integrity monitoring and data risk mitigation in conjunction w/IS Guidance and Policy

• Meet synthetic data KPIs for quality, performance, and service uptime.
• Reduce dependency on core IT architecture for AI model development.
• Maintain compliance and operational stability of the synthetic data ecosystem.

Required Qualifications

• Education: Master’s degree required.
• Experience: 2–3 years of relevant experience with some leadership exposure, ideally in data engineering, architecture, synthetic data systems, or compliant data environments.
• Technical Capability: Experience in data engineering, synthetic data pipelines, ETL/ELT workflows, or regulated healthcare data systems.
• Compliance Knowledge: Familiarity with PI/PHI handling, HIPAA, or regulated‑data architectures preferred.
• Leadership: Ability to mentor junior engineers/architects and coordinate technical backlog or system maintenance cycles.

The Medical University of South Carolina is an Equal Opportunity Employer. MUSC does not discriminate on the basis of race, color, religion or belief, age, sex, national origin, gender identity, sexual orientation, disability, protected veteran status, family or parental status, or any other status protected by state laws and/or federal regulations. All qualified applicants are encouraged to apply and will receive consideration for employment based upon applicable qualifications, merit and business need.

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