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.
Technical Skills
Semantic Technologies: RDF, OWL, SKOS, SPARQL, SHACL, Linked Data, JSON-LD
Platforms & Tools: GraphDB, TopBraid, CenTree, MarkLogic, Protégé, Semaphore, Databricks
Data & Integration: SQL, PySpark, ETL, Git, REST APIs, XML, JSON,
Data Governance: Metadata management, Data stewardship workflows, Controlled vocabulary governance, Data lineage, Data quality rules and validation
Professional Certifications
Databricks Certificate preferred
SAFe® Practitioner Certificate preferred
Any Data Analysis certification (SQL, Python)
Data Governance, Data Management, or Data Stewardship certification or semantic technology-related certification, where applicable
Soft Skills
Strong analytical abilities to assess and improve master data processes and solutions.
Excellent verbal and written communication skills, with the ability to convey complex data concepts clearly to technical and non-technical stakeholders.
Effective problem-solving skills to address data-related issues and implement scalable solutions.
Ability to work effectively with global, virtual teams
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
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