Associate Director, Data Strategy and Governance
Summary
Senior data leadership role at Amgen shaping the enterprise-wide data strategy for AI enablement — building AI-ready data products, governance frameworks, master/metadata management, and semantic layers. Day to day involves driving data maturity programs, ROI/value frameworks, and innovation pilots, leveraging platforms like Collibra, Informatica, and Databricks.
Job description for Associate Director Data Strategy in Data Foundation and Governance
The Associate Director, Enterprise Data Strategy AI Enablement, is responsible for shaping and executing the enterprise-wide data strategy that accelerates AI adoption, digital transformation, and business value realization across the organization.
This role serves as a strategic bridge between Business, Data, Technology, Analytics, and AI teams to establish an AI-ready data ecosystem built on trusted data products, active metadata, semantic knowledge layers, governance by design, and modern data management practices.
The leader will drive enterprise data maturity, define future-state capabilities, enable responsible AI, and create measurable business outcomes through data and AI investments.
Key Responsibilities
Contribute to defining and evolve the Enterprise Data AI-readiness Strategy aligned with business priorities and digital transformation goals
Drive FAIR maturity assessments and continuous improvement programs
Partner with business leaders to identify strategic opportunities where data and AI can create competitive advantage
Help in creation and execution of multi-year data strategy execution roadmap for Amgen
covering Data Products, Data Governance, Enterprise Master data, Metadata management, Reference data including Ontologies and Knowledge Graphs, Data Quality and Data observability
Establish and drive adoption of enterprise-wide frameworks for modern data management practices such as AI-ready data, data products, context engineering, unstructured data, and semantic modelling
Define data product lifecycle, ownership, governance, funding, and value realization frameworks
Establish and operationalize enterprise Data Product Management capabilities
Enable domain-centric data ownership and scalable data product delivery models
Define enterprise framework for measuring Return on Data and AI-readiness Investments with outcomes clearly linked to KPIs around revenue growth, cost optimization, productivity gains, business adoption, and impact
Develop executive dashboards highlighting value realization from strategic data initiatives
Help modernize governance from policy-driven to intelligence-driven governance
Drive modernization of structured and unstructured data management capabilities
Contribute to defining the target-state architecture required to support Generative AI, Agentic AI, Predictive AI, and Advanced Analytics
Implement governance-by-design principles leveraging automation and active metadata
Partner with Legal, Privacy, Compliance, and Risk teams to establish AI-readiness controls, Data Ethics, Regulatory compliance frameworks
Identify and develop pilots for emerging technologies and trends in areas such as Agentic AI, Autonomous data management, augmented data quality, Active metadata, and data observability
Drive Data management pilots, MVPs, and innovation initiatives that demonstrate measurable business value
Build enterprise capabilities for AI-enabled data management operations
Influence executive stakeholders and build alignment across global teams.
Serve as a trusted advisor to senior leadership on data and AI-readiness strategy
Promote a culture of innovation, experimentation, and data-driven decision making
Mentor and develop next-generation data and AI leaders
Represent the organization in industry forums and external thought leadership initiatives
Preferred Qualifications
16 to 20 years of experience in Data Management, Data Strategy, Analytics, or Digital Transformation
5+ years leading enterprise-scale Data and AI transformation initiatives.
Experience in Life Sciences, Healthcare, Pharmaceutical, or highly regulated industries preferred.
MBA or Masters in relevant field preferred
Excellent stakeholder management and communication skills
Demonstrable experience in value articulation of data management initiatives
Exposure to complex stakeholder ecosystem
Strong expertise in several of the following:
Enterprise Data Strategy
Data Governance
Data Products
Master Data Management
Metadata Management
Reference data management and Knowledge Graphs
Data Quality Observability
Unstructured Data Management
AI Modern Data Capabilities
Good knowledge of one or multiple Data Management platforms such as Collibra, Informatica, Ataccama, Reltio, Centree, DataBricks etc.
Basic understanding of Generative AI, Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Semantic Layer Architecture, Active Metadata Platforms