Lead - Biomedical Data Factory Team

Medical University of South Carolina•Charleston, SC

1d•Onsite

About The Position

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.

Requirements

Master’s degree required.
2–3 years of relevant experience with some leadership exposure, ideally in data engineering, architecture, synthetic data systems, or compliant data environments.
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.
Ability to mentor junior engineers/architects and coordinate technical backlog or system maintenance cycles.

Responsibilities

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.
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.
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.
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.
Maintain data governance practices that support ethical model development.
Support model integrity monitoring and data risk mitigation in conjunction w/IS Guidance and Policy.