Reference Data Governance Specialist

Role Description:

Amgen is seeking an experienced Reference Data Governance specialist to support enterprise semantic data, ontology, taxonomy, and knowledge graph initiatives across business and scientific domains.

The professional will work closely with business SMEs, data stewards, data architects, governance teams, and technology partners to define, standardize, govern, and publish reference data across enterprise platforms. As a member of the Reference Data Product team within the Data Foundations & Governance organization, you will be responsible for managing the end-to-end reference data lifecycle, promoting reuse of governed reference data, supporting semantic interoperability, and enabling enterprise-wide adoption of controlled vocabularies, taxonomies, ontologies, and metadata standards.

Roles & Responsibilities:

Design, build, maintain, and govern enterprise reference data models, controlled vocabularies, taxonomies, ontologies, and semantic data assets

Contribute towards defining Reference data management framework for Enterprise

Manage the reference data lifecycle including intake, assessment, standardization, approval, stewardship, versioning, publishing, monitoring, and retirement.

Partner with business SMEs, data owners, data stewards, and governance councils to define reference data standards, ownership, stewardship rules, and usage guidelines.

Define and operationalize federated governance model for Reference Data

Define, manage and govern enterprise reference data products and the reference data within the knowledge layer

Support enterprise reference data governance processes, including data quality rules, issue management, change management, lineage, impact assessment, and compliance tracking.

Apply FAIR Data Principles to ensure reference data is findable, accessible, interoperable, and reusable across business and scientific domains.

Develop and optimize RDF/OWL/SKOS-based semantic frameworks and SPARQL/SQL queries.

Support semantic data integration, metadata harmonization, and semantic publishing workflows.

Manage ontology lifecycle activities including modeling, validation, versioning, and publishing.

Collaborate with multiple enterprise & functional teams to elicit, structure and formalize knowledge from domain experts and diverse sources to build CVs, taxonomies, and ontologies.

Troubleshoot semantic data load and mapping issues across Semantic Layer, CDL, and graph platforms.

Identify and resolve complex reference data, metadata, semantic interoperability, and governance-related challenges.

Support enterprise Data Foundations, Knowledge Graph and Metadata Management initiatives.

Participate in sprint planning meetings and provide estimations on technical implementation.

Basic Qualifications and Experience:

Master’s degree with 7- 10 years of experience in Business, Engineering, IT or related field OR

Bachelor’s degree with 8 - 12 years of experience in Business, Engineering, IT or related field OR

Functional Skills:

Must-Have Skills:

Mandatory: 8–12 years of experience in Reference Data Management, Data Governance, Semantic Technologies, or Knowledge Graph implementations.

Strong experience in enterprise reference data governance, including stewardship workflows, ownership models, approval processes, data quality management, versioning, and publishing.

Advanced understanding of reference data lifecycle management across enterprise, clinical, regulatory, commercial, research, and scientific domains.

Advanced knowledge of ontologies and taxonomies with proficiency in Semantic Web technologies, standards and tools such as RDF/s, OWL, SKOS, SPARQL, SHACL, and Linked Data standards.

Hands-on experience with GraphDBs, TopBraid EDG, CenTree, MarkLogic, Stardog, or similar semantic platforms.

Working knowledge of FAIR Data Principles and experience applying FAIR concepts to reference data, metadata, controlled vocabularies, taxonomies, or ontologies.

Strong expertise in Pharma Domain CVs/Taxonomies/Ontologies such as CDISC, MedDRA, WHODrug, NCIT, IDMP SPOR, SNOMED CT, ICD 10/11 etc. and integrating them into enterprise-wide applications.

Strong understanding of metadata management, reference data governance, and semantic interoperability.

Hands-on experience with modern data platforms such as Databricks and cloud engineering platforms such as AWS.

Strong analytical, troubleshooting, and stakeholder management skills.

Exposure to enterprise knowledge graph and AI/ML initiatives.

Experience working in Agile delivery models and cloud-based ecosystems.