Senior Manager, R&D Data Stewardship

The Senior Manager, R&D Data Stewardship (Clinical & Clinical Operations) is a critical role within a newly created TES (Technology Enabled Sciences) - Data Excellence (DE) group with Center of Excellence (COE) for Data stewardship. This role bridges clinical operations strategy, regulatory compliance, and enterprise data architecture to ensure clinical trial data is FAIR (Findable, Accessible, Interoperable, Reusable), AI-ready, and strictly governed.

Acting as the definitive stewardship voice for clinical data, this individual will partner cross-functionally to enforce data standards (e.g., CDISC, eCOA), author governance procedures, and oversee curation of Clinical Master Data Management (MDM) entities. This is a strategic and procedural oversight role; it does not involve hands-on operational trial execution, database programming, or data entry.

Responsibilities

Strategic Governance & Interoperability

• Partner with the Data Excellence (DE) Clinical Lead to operationalize the clinical data strategy, ensuring portfolio priorities are translated into daily stewardship practices.
• Act as a primary stewardship liaison for Clinical and Clinical Operations data, aligning closely with Research, Regulatory/Safety Stewards, and technical teams.
• Drive the adoption and enforcement of clinical data standards (e.g., CDISC, CDASH, SDTM, ADaM, PHUSE, TransCelerate) across the R&D ecosystem.
• Provide stewardship oversight for Master Data Management (MDM) entities critical to clinical operations.
• Govern access policies and data handling rules for high-risk clinical data (e.g., PII, randomization data), ensuring strict blinding integrity and controlled access for Data Monitoring Committees (DMC) and safety reviews.
• Execute on stewardship decision rights for "standard vs. study-specific" determinations, deviation acceptability, and escalation recommendations to governance forums.
• Partner with the DE Clinical Lead to ensure governance decisions are consistently translated into usable guidance, with rationale/explainability and serve as final clinical standards interpretation authority (internal + external -CRO).

• Establish clear enterprise definitions and lineage traceability for clinical data types (e.g., raw source, eSource, derived, and owned data) across the R&D data lifecycle.
• Collaborate with the Technical Steward to map clinical metadata and data lineage into enterprise governance platforms (e.g., Informatica CDGC, Centree) and the metadata repository.
• Author, review, and maintain foundational process documentation, including Standard Data Specification Plans (SDSPs), SOPs, and Work Instructions (WIs) for dictionary management (MedDRA, WHODrug) and metadata workflows.
• Define metadata standards and governance policies for eCOA/ePRO data, including validated instrument implementation, scoring conventions, versioning/translation control, and missing data conventions.
• Govern Data Transfer Specifications (DTS) and Data Quality Rules (DQR) to ensure external vendor data and operational metrics meet internal standards upon ingestion.
• Actively support cross-functional governance review boards, advising on data models and standards.

• Bachelor's or Master's degree in Life Sciences, Computer Science, Data Science, or a related field.
• 5-8+ years of experience in the pharmaceutical/biotech industry, specifically focused on clinical data standards, metadata management, or R&D data governance.
• Deep knowledge of global regulatory expectations related to clinical data systems, explicitly including ICH E6(R3)/E8(R1) ALCOA+ principles, 21 CFR Part 11 (audit trails/eSignatures), and data privacy controls (GDPR).
• Deep functional knowledge of global clinical data frameworks (CDISC, CDASH, SDTM) and regulatory submission requirements.
• Experience governing clinical ontologies/vocabularies (e.g., SNOMED, MedDRA, NCIt, OBO).
• Strong understanding of Clinical Operations workflows and the data pipelines required to feed operational dashboards.
• Exceptional ability to translate technical constraints to business stakeholders and clinical requirements to data engineering teams.

• Clinical COTS Ecosystems: Familiarity with the data structures of major clinical SaaS platforms, including Veeva (CTMS/eTMF), Suvoda (IRT), and the Sycamore Informatics clinical suite (CDW, MDR, SCE).
• Modern Data Platforms: Exposure to structured data environments (e.g., Databricks, AWS Redshift) and an understanding of how clinical data is curated within Medallion architectures.
• Unstructured & Advanced Data: Foundational understanding of governing unstructured data storage (e.g., AWS S3) and semantic relationships within Knowledge Graphs.
• Governance Tooling: Hands-on experience with enterprise cataloging (Informatica CDGC) and workflow automation (ServiceNow) platforms.