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How much do neo4j knowledge graph jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for neo4j knowledge graph in the United States is $61.83, according to ZipRecruiter salary data. Most workers in this role earn between $55.29 and $67.79 per hour, depending on experience, location, and employer.

What is a Neo4j knowledge graph?

A Neo4j Knowledge Graph is a data representation approach that uses the Neo4j graph database to model, store, and query complex relationships between entities. Unlike traditional databases, Neo4j organizes data as nodes and relationships, making it ideal for connecting information and uncovering hidden patterns. Knowledge graphs built with Neo4j are widely used for applications such as recommendation systems, fraud detection, and semantic search. They allow organizations to gain deeper insights by visualizing and querying interconnected data efficiently.

What are some typical challenges faced when implementing and maintaining Neo4j knowledge graphs in an enterprise environment?

One common challenge is ensuring data consistency and integrity as the graph grows and new data sources are integrated. Professionals working with Neo4j knowledge graphs often need to collaborate closely with data engineers, domain experts, and developers to design an effective data model and maintain optimal performance. Regularly updating and optimizing Cypher queries, managing access controls, and keeping the graph schema aligned with evolving business needs are also key responsibilities. Staying up-to-date with best practices and new Neo4j features can significantly ease these challenges and support successful project delivery.

What are the key skills and qualifications needed to thrive as a Neo4j knowledge graph engineer, and why are they important?

To excel as a Neo4j Knowledge Graph Engineer, you need strong skills in graph data modeling, Cypher query language, and database management, often supported by a degree in computer science or a related field. Familiarity with Neo4j tools, graph database platforms, and certifications like Neo4j Certified Professional are highly valued. Analytical thinking, problem-solving, and effective communication help you translate complex relationships into actionable insights and collaborate with cross-functional teams. These competencies are crucial for designing efficient knowledge graphs, ensuring data integrity, and enabling advanced data-driven decision-making.

What is the difference between Neo4J Knowledge Graph vs Data Scientist?

AspectNeo4J Knowledge GraphData Scientist
Required CredentialsGraph database knowledge, often certifications in Neo4JStatistics, programming, data analysis degrees or certifications
Work EnvironmentPrimarily working with graph databases, data modeling, and queryingData analysis, modeling, and predictive analytics in various tools
Industry UsageUsed in data integration, knowledge management, and graph analyticsApplied across industries for insights, forecasting, and decision-making

Neo4J Knowledge Graph specialists focus on designing and querying graph databases, while Data Scientists analyze data to extract insights. Both roles require strong analytical skills but differ in tools and focus areas.

What other helpful pages are available for Neo4J Knowledge Graph?

Other pages related to Neo4J Knowledge Graph:

Infographic showing various Neo4J Knowledge Graph job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 78% Full Time, 17% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $128,609 per year, or $61.8 per hour.

Ontology / Knowledge Graph Engineer

Chatham, NJ • On-site

2T Consulting
IT Services • 51 - 200 employees
Learn more about 2T Consultingexternal

Full-time

Posted 6 days ago

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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