Lead, Synthetic Data Pipelines & AI Infrastructure

Medical University of South Carolina; Charleston, Northern (SC, KY); Hybrid; USD 130,000 - 170,000; Full time

Job summary

Medical University of South Carolina (MUSC) seeks a Lead of the Clinical Data Factory to advance the data ecosystem powering AI development. You will ensure PI/PHI‑compliant synthetic data pipelines, support model-training readiness, and maintain data availability in alignment with IS architecture.

You will lead a team of data engineers and architects, collaborate with the AI Center, and drive governance to ensure compliant, high‑integrity data for experimentation and modeling efforts.

Qualifications

• Master’s degree required.
• 2–3 years of relevant experience with some leadership exposure.
• Experience in data engineering, synthetic data pipelines, ETL/ELT workflows, or regulated healthcare data systems.
• Familiarity with PI/PHI handling, HIPAA, or regulated‑data architectures preferred.

Responsibilities

• Lead Synthetic Data Pipeline Management & Compliance initiatives and ensure PI/PHI compliant data flows.
• Oversee System Architecture, maintenance and enhancements for AI Center tools and data infra.
• Provide Team Leadership & technical guidance to Jr. Data Engineers and Architects.
• Collaborate with AI project teams to support data provisioning, synthetic layering and model experimentation.
• Support Governance, Quality & Model Integrity aligning with IS Guidance and policy.

Skills

Data engineering

Synthetic data pipelines

ETL/ELT workflows

Leadership

Education

Master’s degree

Job description

Medical University of South Carolina (MUSC) seeks a Lead of the Clinical Data Factory to advance the data ecosystem powering AI development. You will ensure PI/PHI‑compliant synthetic data pipelines, support model-training readiness, and maintain data availability in alignment with IS architecture.

You will lead a team of data engineers and architects, collaborate with the AI Center, and drive governance to ensure compliant, high‑integrity data for experimentation and modeling efforts.