Manager of Clinical Research Data Warehousing

Strategic Leadership & Institutional Alignment

Define and execute the strategic roadmap for the clinical research data warehouse, focusing on:

• AI/ML-ready data architectures
• Scalable analytics and research enablement
• Interoperability and common data models
• Collaborate with senior academic and hospital leadership to align data warehousing priorities with institutional research, clinical, and translational goals.
• Serve as a trusted partner to faculty leadership and mentors, advising on data feasibility, analytic approaches, and emerging capabilities.
• Represent the data warehousing function in enterprise-level discussions related to informatics strategy, data harmonization, and AI readiness.

Matrixed & Cross-Functional Collaboration

Operate effectively in a matrixed environment, coordinating across reporting lines, service teams, and governance bodies.

• Collaborate closely with application development teams to align data pipelines, APIs, and research platforms.
• Collaborate with HPC and scientific computing experts to support large-scale analytics and AI/ML workflows.
• Collaborate with bioinformatics and data science teams to integrate clinical data with multi-modal research datasets.
• Collaborate with faculty investigators and research teams to translate funded research aims into data and analytic solutions.
• Act as a connector and translator between technical teams, researchers, and leadership.

Data Architecture, Modeling & Interoperability

Provide architectural oversight for the design and optimization of clinical research data assets.

• Lead adoption and governance of common data models (e.g., OMOP, PCORnet, or equivalent).
• Ensure analytic fitness for research and AI use cases.
• Advance interoperability strategies leveraging standards such as FHIR, modern APIs, and modular data services.
• Ensure documentation, data provenance, and metadata practices support reproducibility, reuse, and responsible AI development.

ETL Oversight & Technical Design Optimization

Oversee (but do not primarily perform) the development and optimization of ETL pipelines ingesting data from Epic EMR systems (e.g., Clarity, Caboodle, Cosmos) and other sources.

• Set technical standards, review designs, and guide implementation decisions to ensure performance, reliability, and scalability.
• Partner with engineers to modernize pipelines using automation, cloud-native patterns, and best practices in data engineering.
• Ensure strong data quality, validation, and refresh processes aligned with funded research commitments.

Research Enablement & Faculty Support

Directly support faculty-funded research, ensuring data assets meet grant timelines, deliverables, and compliance requirements.

• Advise investigators and project teams on cohort discovery, longitudinal analysis, and real-world data use.
• Enable AI- and ML-driven research by ensuring datasets are analytically valid, well-structured, and performance-optimized.
• Balance self-service data access with appropriate governance and stewardship.

Management, Operations & Recharge Center Responsibilities

Lead, mentor, and develop a team of data engineers, analysts, and related staff.

• Prioritize work across competing research and institutional demands in a transparent, service-oriented model.
• Operate within a federal recharge center, including:
• Supporting sustainable cost-recovery models.
• Allign effort with funded work and service agreements.
• Partnership on budgeting, forecasting, and reporting.
• Collaboration with governance, privacy, security, and compliance teams to ensure responsible data use.
• Contribute to continuous process improvement and service maturity.
• Manage professional staff.
• Establishes performance goals, allocates resources and assesses policies for direct subordinates.
• Recommend departmental plans to maintain administrative data.
• Ensures that the data is accessible, easy-to-use, flexible, and suitable for various analytical purposes, including joint analyses across multiple domains and interactions across multiple systems.
• Plans additional data warehouse and reporting environments as needed.
• Manages relationships with the University's primary software suppliers for end-user data access, query, reporting, and display.
• Perform other related work as needed.