Semantic Data Modeller

Job Description

Role- Semantic Data Modeller

Experience- 5-10 years”

Location- Pune, Hyderabad, Bangalore

Required Skills & Experience

Must Have

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• 5-10 years of data modelling or related experience, including meaningful hands-on work in semantic modelling, ontology engineering, or knowledge representation.
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• Practical BFSI experience in at least one area such as banking, lending, payments, risk, capital markets, insurance, or regulatory data.
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• Strong understanding of conceptual, logical, and semantic data modelling, taxonomies, controlled vocabularies, ontologies, and knowledge graphs.
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• 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.
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• Hands-on experience authoring semantic models or ontologies using Protégé, WebProtégé, TopBraid, Stardog Studio, or a comparable tool.
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• Ability to facilitate SME workshops, define competency questions, resolve terminology conflicts, and document modelling decisions clearly.
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• Understanding of ontology governance, validation, versioning, traceability, Git-based lifecycle management, and collaborative review practices.
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• Awareness of how semantic models and knowledge graphs can support semantic search, natural-language querying, RAG, Graph RAG, explainability, or agent workflows.
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Good to Have

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• Exposure to source-to-semantic mapping approaches such as R2RML, RML, YARRRML, Ontop, or OBDA.
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• Working knowledge of one or more knowledge-graph platforms such as Neo4j with Neosemantics, Stardog, GraphDB, Apache Jena, Amazon Neptune, or a comparable platform.
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• Exposure to FIBO, MISMO, insurance ontologies, ISO standards, regulatory taxonomies, or enterprise business glossaries.
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• Working knowledge of Python and semantic libraries such as RDFLib, OWLready2, pySHACL, or equivalent tooling.
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• Exposure to Cypher, Gremlin, graph visualization, entity resolution, graph analytics, or graph database modelling.
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• Understanding of metadata platforms, data catalogs, lineage, lakehouse architectures, vector databases, embeddings, and LLM orchestration frameworks.
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• Experience delivering semantic assets for underwriting, mortgage, customer, product, risk, compliance, fraud, AML, or related BFSI use cases.
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Education

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• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, Mathematics, or a related discipline.
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