Manager of Clinical Research Data Warehousing

* Strategic Leadership & Institutional Alignment
* Under the direction of CRI leadership, define and execute the strategic roadmap for the clinical research data warehouse, with explicit focus 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.
* In coordination with CRI leadership and the technical manager of data warehousing, 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
* HPC and scientific computing experts to support large-scale analytics and AI/ML workflows
* Bioinformatics and data science teams to integrate clinical data with multi-modal research datasets
* 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) and 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
* Partnering 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…