Semantic Data Architect

Job Description: Semantic Data Architect

Primary skill :Semantic Data Architect

Position Summary

We are seeking a Semantic AI/ML architect to design, develop, and operationalize semantic models that connect enterprise data across structured and unstructured sources.

This role will create ontologies, taxonomies, semantic models, and knowledge graphs that improve data discovery, governance, integration, compliance, analytics, and AI use cases. The successful candidate will work across data governance, architecture, engineering, and business domains to translate business concepts into reusable semantic assets.

Key Responsibilities

• Ontology and Semantic Modelling: Develop enterprise and domain ontologies representing business concepts, entities, relationships, classifications, and business rules.
• Knowledge Graph Engineering: Design and implement knowledge graphs using RDF, property graph, or hybrid approaches. Leverage ontologies to build KGs inking diverse data into unified graph model.
• Querying and Integrations: Develop SPARQL queries and/or Cypher queries to support graph exploration, analytics, search, and applications. Develop graph data models, ingestion pipelines, transformations, and entity-resolution processes. Load and transform data from relational databases, data lakes, APIs, documents, spreadsheets, and enterprise applications.
• AI workflows: Implement semantic reasoning, inference, validation, and graph rules where appropriate. Build prototypes and production-ready graph solutions using platforms such as Stardog, Neo4j, or comparable technologies.

Required Skills

• Atleast 7+ years of experience in Data Engineering including Python and SQL, ETL and ELT pipelines, APIs and data integration, Data Lake and warehouse technologies, Streaming or event-based data integration,
• Experience designing ontologies, taxonomies, controlled vocabularies, semantic models, or knowledge graphs.
• Experience working on projects that involve AI agents or large language models, where ontologies or knowledge graphs were used to improve AI performance
• Practical experience with one or more of the following: RDF, RDFS, OWL, SPARQL, SHACL, SKOS, JSON-LD or Cypher
• Experience with one or more graph or semantic platforms, such as: Stardog, Neo4j, Amazon Neptune, GraphDB, Ontotext or Other RDF or property graph technologies
• Understanding of data modelling principles, including conceptual, logical, physical, and semantic modelling.
• Ability to communicate effectively with both business stakeholders and technical teams.
• Experience documenting models, mappings, architecture, and governance processes.