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Knowledge Graph Engineer Jobs in New Jersey (NOW HIRING)

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Infographic showing various Knowledge Graph Engineer job openings in New Jersey as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 16% Part Time, and 2% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution.

Ontology / Knowledge Graph Engineer

2T Consulting

Mount Tabor, NJ • On-site

Full-time

Posted 2 days ago

New

Job description

We are seeking an experienced Ontology / Knowledge Graph Engineer with strong expertise in ontology engineering, semantic modeling, and knowledge graph development. The ideal candidate will have hands-on experience designing and implementing ontology-driven knowledge graphs using standards such as OWL, RDF, SPARQL, SHACL, and JSON-LD.

Required Skills
  • Strong experience in Ontology Engineering and ontology-driven knowledge graph design.
  • Expertise in Knowledge Modeling and Semantic Modeling.
  • Hands-on experience with OWL, RDF, SPARQL, SHACL, and JSON-LD.
  • Experience with Ontological Inference and consistency checking.
  • Strong knowledge of Knowledge Graphs, RDF Graphs, and Property Graphs.
  • Experience with Graph Data Modeling and Graph Analytics.
  • Hands-on experience with Entity Resolution.
  • Ability to design and implement scalable semantic and knowledge graph solutions.
Technologies / Tools
  • Protégé
  • TopBraid Composer
  • OntoStudio
  • Neo4j
  • Stardog
  • GraphDB
  • Apache Jena
  • Fuseki
  • Blazegraph
  • Virtuoso
Key Responsibilities
  • Design, develop, and maintain enterprise ontologies and semantic models.
  • Build ontology-driven Knowledge Graph (KG) solutions aligned with business and technical requirements.
  • Develop and manage RDF-based knowledge graphs using OWL, RDF, SPARQL, SHACL, and JSON-LD.
  • Implement ontological inference and reasoning capabilities.
  • Perform consistency checking and validation of ontologies and knowledge graph data.
  • Develop graph data models and support both RDF and property graph architectures.
  • Implement entity resolution and semantic relationships across disparate data sources.
  • Perform graph analytics to derive insights from connected data.
  • Use ontology and knowledge graph tools such as Protégé, TopBraid Composer, Stardog, GraphDB, Neo4j, and Apache Jena.
  • Collaborate with data engineers, architects, and business stakeholders to define semantic requirements and modeling standards.
  • Establish best practices for ontology governance, versioning, validation, and knowledge graph quality.

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