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 closely with HPC and scientific computing experts to support large-scale analytics and AI/ML workflows.
  • Collaborate closely with bioinformatics and data science teams to integrate clinical data with multi-modal research datasets.
  • Collaborate closely 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.
  • Aligning effort with funded work and service agreements.
  • Partnership on budgeting, forecasting, and reporting.
  • Collaborate with governance, privacy, security, and compliance teams to ensure responsible data use.
  • Contribute to continuous process improvement and service maturity.
  • Manages professional staff. Establishes performance goals, allocates resources and assesses policies for direct subordinates.
  • Recommends 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.
  • Performs other related work as needed.