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Tier-1 employer and Hyderabad metro increase density, but niche semantic/knowledge-graph skills limit competition.
Strong pharma-specific ontologies, regulatory CVs, and domain knowledge make cross-industry transfer limited.
Explicit 8–12 years, mandatory reference-data, semantic tech, and pharma domain expertise create high shortlisting strictness.
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Own and govern enterprise reference data lifecycle including intake, standardization, stewardship, versioning, publishing, and retirement across business and scientific domains.
Design, maintain, and manage semantic data assets such as ontologies, taxonomies, controlled vocabularies; apply Semantic Web standards and ensure FAIR Data Principles adoption.
Collaborate with SMEs, data stewards, governance teams to define standards, stewardship rules, and enable enterprise-wide controlled vocabulary and metadata standards adoption.
8–12 years of experience in Reference Data Management, Data Governance, Semantic Technologies, or Knowledge Graph implementations.
Bachelor’s or Master’s degree in Business, Engineering, IT or related field (Master’s with 7-10 years or Bachelor’s with 8-12 years experience).
Strong hands-on skills with Semantic Web technologies (RDF, OWL, SKOS, SPARQL) and platforms such as GraphDB, TopBraid, MarkLogic, Stardog.
Working knowledge of FAIR Data Principles and pharma domain vocabularies (e.g., CDISC, MedDRA, WHODrug, SNOMED CT).
Experienced in managing enterprise-wide reference data governance frameworks, with deep domain expertise in pharmaceutical data standards and semantic interoperability.
Proficient in advanced semantic technologies, ontology lifecycle management, and cloud data platforms (e.g., Databricks, AWS).
Comfortable navigating Agile delivery environments, collaborating with global stakeholders, and troubleshooting complex semantic data integration challenges.