You will play a key role in implementing and driving adoption of Amgen's data governance framework, modernizing the data ecosystem to position Amgen as a leader in biopharma innovation. Leveraging advanced technologies such as Generative AI and Machine Learning, you will provide expert domain, technical, and business process support. This role requires close collaboration with business stakeholders, data analysts, Product Data Owners, and data leaders to increase data trust and reuse across Amgen. Roles & Responsibilities Lead adoption of the data governance framework for a specific domain (e.g., Research, Development, Supply Chain). Operationalize the enterprise data governance framework, aligning stakeholders on data governance needs, including: Data quality Data access controls Privacy and security compliance Master data management Data sharing Communication and change management Collaborate with Enterprise MDM and Reference Data teams to enforce data standards and promote reusability. Drive cross-functional alignment within assigned domains to ensure adherence to data governance principles. Develop policies covering data, metadata, privacy, lineage, access, security, retention, and archival. Maintain detailed documentation of: Data definitions and standards Data flows Legacy data structures and hierarchies Common data models and harmonization Ensure compliance with data privacy, security, and regulatory policies within assigned domains. Partner with Technology, business, and enterprise teams (e.g., MDM, Data Fabric) to define specifications for data foundation development and implementation. Build and maintain strong relationships with key business leads and partners to meet their data governance needs. Must-Have Functional Skills Technical expertise in advanced SQL, Python, etc., with deep knowledge of Pharma processes, specializing in domains such as Research, Clinical Trials, Commercial, etc. Extensive experience working with or supporting data governance tools like Collibra or Alation. In-depth knowledge of data management concepts including: Common data models Metadata management Data quality Master data management Data stewardship Data protection Experience managing the data product development lifecycle, enabling data dictionaries and business glossaries to increase reusability and literacy. Customer-focused with excellent written and verbal communication skills, able to work confidently with internal stakeholders and external service partners on business and technology topics. Ability to manage Scrum teams. Good-to-Have Functional Skills Experience working with data governance councils or forums. Familiarity with Agile software development methodologies such as Scrum. Soft Skills Excellent analytical skills. Ability to work effectively with global, virtual teams. Strong multitasking and priority management capabilities. Team-oriented with a focus on achieving shared goals. Ability to build business relationships and understand end-to-end data use and requirements. Excellent interpersonal and people management skills (matrix or direct line function). Strong verbal and written communication skills. Basic Qualifications Master's degree with 8-10 years experience in Data Science, Artificial Intelligence, Computer Science, or related fields OR Bachelor's degree with 10-14 years experience in Data Science, AI, Computer Science, or related fields OR Diploma with 14-16 years hands-on experience in Data Science, AI/ML technologies, or related technical domains.