Reference Data Specialist

Career Category

Job Description

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.

ABOUT THE ROLE

Role Description:

Amgen is seeking an experienced Reference Data specialist to support enterprise semantic data, ontology, taxonomy, and knowledge graph initiatives across business and scientific domains. The role will focus on designing, developing, governing, and optimizing enterprise reference data and semantic models supporting analytics, interoperability, and data governance initiatives.

As the Reference Data Product team member of the Data Foundations & Governance organization, you will be responsible for managing and promoting the use of reference data, partnering with business Subject Mater Experts on creation of vocabularies / taxonomies and ontologies, and developing analytic solutions using semantic technologies.

Roles & Responsibilities:

• Design, build and maintain enterprise ontologies, taxonomies, and semantic reference data models
• Collaborate with business SMEs, data architects, and governance teams to define semantic standards
• Contribute towards defining Reference data management framework for Enterprise
• 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.
• Develop and optimize automated data ingestion / pipelines through Python/PySpark when APIs are available
• Support enterprise Data Foundations, Knowledge Graph and Metadata Management initiatives.
• Mentor junior engineers and provide technical leadership on semantic technologies.
• Identify and resolve complex data-related challenges 
• 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 Engineering, Semantic Technologies, or Knowledge Graph implementations.
• 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.
• 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.
• Experience with APIs, JSON-LD, XML, SQL, and semantic integration frameworks.
• Strong analytical, troubleshooting, and stakeholder management skills.
• Familiarity with FAIR Data Principles, Semantic Layer, metadata repositories, data catalogs and semantic publishing pipelines.
• 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, Metadata & Data Governance 

Professional Certifications:

• Databricks Certificate preferred 
• SAFe® Practitioner Certificate preferred 
• Any Data Analysis certification (SQL, Python)
• Any cloud certification (AWS or AZURE)

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