Job Description
Data Platforms & Governance within Animal Health IT is seeking a Director, Data Governance & Stewardship to lead the governance, master data and stewardship capabilities that make Animal Health data trusted, accessible and AI-ready at scale.
The role will co-shape and operationalize federated data governance with business data owners and cross-functional partners across Commercial, Manufacturing & Supply, R&D and Technology Solutions, covering master data, data quality, metadata and lineage, access governance, policies and regulatory readiness. It will partner with the Enterprise Data Quality & Data Stewardship Center of Excellence to adopt enterprise frameworks and tools while addressing Animal Health–specific needs. The role will also define and drive execution of an AI-enabled governance and stewardship strategy, transforming ways of working while strengthening the business metadata and context needed for AI-ready data.
As a member of the Data Platforms & Governance leadership team within the Data, Analytics & AI Hub, you will operate in a global, highly matrixed environment where data governance is designed to enable speed, reuse and accountability.
What will you do
1. Governance Strategy & Operating Model
- Define and operationalize the federated data governance model across global, regional and local levels, including decision rights, stewardship roles, governance forums.
- Partner with the enterprise Data Stewardship CoE to adopt enterprise policies, standards, tooling and shared services, align service expectations and address Animal Health–specific needs without duplicating enterprise capabilities.
- Lead implementation of governance policies and controls for privacy, GxP, cybersecurity, data residency, retention and appropriate data use in partnership with accountable functions, ensuring audit readiness and governance that enables speed and reuse.
2. Master Data, Quality & Trust
- Own master and reference data management practices for Customer, Product, Supplier, Location, Material and other priority entities, partnering with Data Engineering on technical implementation.
- Define reusable rollout patterns for data models, match and merge, survivorship, hierarchies, golden records and stewardship workflows.
- Establish risk-based data quality priorities, preventive controls, monitoring, scorecards and service levels; lead issue management and root-cause remediation with data owners and source-system teams to prevent recurring defects.
3. Metadata, Access & Data Enablement
- Drive business glossary, catalog, semantic standards, metadata and end-to-end lineage across source systems, data products and AI use cases, including consistent business definitions, semantics and context to enable AI-ready data and reliable reuse in partnership with Data Engineering and AI & Analytics.
- Define data product certification and lifecycle governance with product and engineering teams, including quality expectations, usage guidance and retirement; enable governed discovery and subscription through the enterprise data marketplace.
- Own the access governance model, including classification, role- and attribute-based policies, automated provisioning, periodic access reviews and transparent audit reporting.
- Define the strategy and drive execution of AI-enabled governance and stewardship transformation with enterprise and engineering partners, redesigning workflows and scaling proven approaches that improve data quality and reduce manual effort while maintaining human oversight, traceability and appropriate controls.
4. Stewardship, People & Stakeholder Leadership
- Build a global stewardship network and community of practice across the Hub and business Spokes, with clear owner and steward roles, practical playbooks, onboarding, coaching and training.
- Lead and develop a global team spanning data governance, MDM, stewardship and domain-aligned data enablement resources.
- Staff data product squads with governance and stewardship expertise in alignment with domain data leads, embedding shared practices through coaching and hands-on support in a matrix environment.
- Align business, data product, engineering, cybersecurity and enterprise stakeholders, translating governance trade-offs into clear business decisions.
- Establish and review measures of stewardship coverage, adoption, maturity and business impact, using stakeholder feedback and service performance to prioritize continuous improvement.
Primary Skills & Experience (Must-Have)
- Data Governance Leadership: 10+ years in data governance, master data management or data management, including 5+ years leading teams in a global organization.
- Master Data Management: Proven experience designing and operating MDM practices for customer, product or other enterprise domains, including governance, stewardship workflows, match and merge, and hierarchy management.
- Data Quality, Metadata & Lineage: Track record implementing enterprise data quality frameworks, catalogs, business glossaries and end-to-end lineage.
- Access Governance & Privacy: Experience with policy-based access control, auditable provisioning, data classification and privacy requirements.
- Federated Governance: Experience establishing practical governance and stewardship operating models, partnering with an enterprise data office or stewardship center of excellence.
- Regulated Environments: Experience delivering in pharmaceutical, life sciences or another highly regulated industry, including GxP, GDPR and audit requirements.
- Technology Understanding: Working knowledge of cloud data platforms, lakehouse architectures, MDM tools, data catalogs and governance technologies.
- Operational Improvement & Automation: Track record translating strategy into execution, simplifying governance and stewardship workflows, setting service and outcome measures, and driving AI-enabled transformation and adoption in partnership with technical teams.
- Matrix & Stakeholder Leadership: Ability to influence senior business and technical stakeholders, establish accountability and lead change, stewardship capability building and adoption across global teams.
- Communication: Excellent written and verbal English; able to make complex governance topics clear, practical and decision-oriented.
Secondary Skills & Experience (Nice-to-Have)
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering or a related field.
- Professional certification such as DAMA CDMP.
- Hands-on exposure to Informatica IDMC, Collibra, Unity Catalog, Immuta or comparable technologies.
- Experience with SAP S/4HANA, Salesforce or Veeva master data.
- Experience with data-as-a-product, certification or enterprise data marketplaces.
- Experience evaluating or scaling AI/GenAI and agentic automation for stewardship, metadata or data quality, with appropriate human oversight and auditability.
- Animal health, pharmaceutical, commercial, manufacturing or R&D domain experience.
Additional location(s) Compensation Information:
Required Skills:
Data Engineering, Data Governance, Data Integrity, Data Management, Data Modeling, Data Monitoring, Data Operations, Data Protection, Data Retention, Data Security, Data Standards, Data Stewardship, Enterprise Data Management, Information Systems, Information Systems Management, Information Technology (IT), IT Governance, Quality Management, Standards Development
Preferred Skills:
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Employee Status:
Regular
Relocation:
VISA Sponsorship:
Travel Requirements:
Flexible Work Arrangements:
Hybrid
Shift:
Valid Driving License:
Hazardous Material(s):
Job Posting End Date:
11/9/2026
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