Semantic Data Modeller

Role- Semantic Data Modeller
Experience- 5-10 years”
Location- Pune, Hyderabad, Bangalore
Required Skills & Experience
Must Have
5-10 years of data modelling or related experience, including meaningful hands-on work in semantic modelling, ontology engineering, or knowledge representation.
Practical BFSI experience in at least one area such as banking, lending, payments, risk, capital markets, insurance, or regulatory data.
Strong understanding of conceptual, logical, and semantic data modelling, taxonomies, controlled vocabularies, ontologies, and knowledge graphs.
Hands-on experience with RDF, RDFS and OWL, including IRIs, namespaces, classes, properties, domains and ranges, restrictions, and ontology modularization; working knowledge of SKOS, SHACL and SPARQL.
Hands-on experience authoring semantic models or ontologies using Protégé, WebProtégé, TopBraid, Stardog Studio, or a comparable tool.
Ability to facilitate SME workshops, define competency questions, resolve terminology conflicts, and document modelling decisions clearly.
Understanding of ontology governance, validation, versioning, traceability, Git-based lifecycle management, and collaborative review practices.
Awareness of how semantic models and knowledge graphs can support semantic search, natural-language querying, RAG, Graph RAG, explainability, or agent workflows.
Good to Have
Exposure to source-to-semantic mapping approaches such as R2RML, RML, YARRRML, Ontop, or OBDA.
Working knowledge of one or more knowledge-graph platforms such as Neo4j with Neosemantics, Stardog, GraphDB, Apache Jena, Amazon Neptune, or a comparable platform.
Exposure to FIBO, MISMO, insurance ontologies, ISO standards, regulatory taxonomies, or enterprise business glossaries.
Working knowledge of Python and semantic libraries such as RDFLib, OWLready2, pySHACL, or equivalent tooling.
Exposure to Cypher, Gremlin, graph visualization, entity resolution, graph analytics, or graph database modelling.
Understanding of metadata platforms, data catalogs, lineage, lakehouse architectures, vector databases, embeddings, and LLM orchestration frameworks.
Experience delivering semantic assets for underwriting, mortgage, customer, product, risk, compliance, fraud, AML, or related BFSI use cases.
Education
Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, Mathematics, or a related discipline.

Work arrangement
No