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How much do knowledge graph jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for knowledge graph in the United States is $31.03, according to ZipRecruiter salary data. Most workers in this role earn between $15.87 and $25.96 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Knowledge Graph position, and why are they important?

To thrive as a Knowledge Graph Engineer, you need strong skills in semantic web technologies, ontology modeling, and data integration, typically supported by a background in computer science or data science. Familiarity with tools like RDF, SPARQL, OWL, and knowledge graph platforms (e.g., Neo4j, GraphDB) is common, and certifications in data engineering or semantic technologies are beneficial. Effective communication, problem-solving abilities, and cross-functional collaboration are valuable soft skills in this field. These competencies are crucial for designing, implementing, and maintaining knowledge graphs that enable advanced data discovery and insights for organizations.

What is a Knowledge Graph job?

A Knowledge Graph job typically involves designing, building, and maintaining structured representations of data that map relationships between entities. Professionals in this role work with technologies like RDF, SPARQL, ontologies, and graph databases to enhance data integration, retrieval, and reasoning. These jobs are common in AI, search, and data science fields, helping organizations improve knowledge discovery and decision-making.

What are some typical daily responsibilities of a Knowledge Graph Engineer?

As a Knowledge Graph Engineer, your typical day involves designing and developing ontologies, integrating diverse data sources, and implementing graph-based data models to enhance information accessibility. You may work closely with data scientists, software developers, and business analysts to gather requirements and translate them into scalable knowledge graph solutions. Regular tasks include writing SPARQL queries, performing data mapping, maintaining documentation, and troubleshooting graph data issues. Collaboration and ongoing learning are integral as this field rapidly evolves with new tools and best practices.

More about Knowledge Graph jobs
What cities are hiring for Knowledge Graph jobs? Cities with the most Knowledge Graph job openings:
What are the most commonly searched types of Knowledge Graph jobs? The most popular types of Knowledge Graph jobs are:
What states have the most Knowledge Graph jobs? States with the most job openings for Knowledge Graph jobs include:
What job categories do people searching Knowledge Graph jobs look for? The top searched job categories for Knowledge Graph jobs are:
Knowledge Graph jobs near you
Infographic showing various Knowledge Graph job openings in the United States as of June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $64,550 per year, or $31 per hour.
Knowledge Graph Engineer / Ontologist

Knowledge Graph Engineer / Ontologist

The Hartford Financial Services Group, Inc.

Hartford, CT • On-site, Remote

Full-time

Posted 21 days ago

The Hartford rating

8.8

Company rating: 8.8 out of 10

Based on 104 frontline employees who took The Breakroom Quiz

53rd of 261 rated insurance

Job description

Dir Data Engineering - GE06AE
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.
The Ontologist (Knowledge Graph Engineer) is responsible for designing, implementing, and operationalizing enterprise semantic models, ontologies, and knowledge representations that provide meaning, context, and explainability for AI-driven analytics, agentic systems, and decision automation.
This role ensures that business concepts, entities, relationships, and behaviors are explicitly modeled and consistently applied across data products and AI systems, enabling reuse, trust, reasoning, and scalable AI adoption across Customer, Operations, and Enterprise domains. This role is part of the Customer Data Ecosystem (CDE) and operates at the intersection of business semantics, data architecture, and AI enablement, translating complex domain knowledge into production-ready semantic assets that are consumable by both humans and machines.
This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Candidates must be eligible to work in the US without company sponsorship.
Primary Job Responsibilities
  • Lead the design and execution of enterprise-scale semantic layers to standardize business meaning and enable trusted analytics, AI, and Agentic use cases.
  • Define and operationalize ontologies, context graphs, and knowledge graphs across domains to power reasoning, explainability, and decision intelligence.
  • Enable semantic-first AI and Agentic analytics, ensuring LLMs and agents can consume governed business context, metrics, and rules.
  • Define canonical semantic vocabularies that standardize meaning across structured and unstructured data sources.
  • Drive production-scale execution of semantic and knowledge platforms with strong standards for performance, governance, security, and lifecycle management.
  • Evangelize Agentic Data Engineering, driving adoption through patterns, playbooks, and real-world deployments across the enterprise.
  • Define and promote standards and best practices for semantic modeling and ontology reuse across delivery teams.
  • Partner with architects and engineers to embed semantic models into data products, AI pipelines, and activation layers.
  • Work closely with AI Data Architects and AI Data Engineers to operationalize ontologies into production systems (e.g., via graphs, metadata services, APIs).
  • Align ontologies with enterprise data governance, lineage, and quality standards.
  • Enable explainability by ensuring AI outputs can be traced back to governed semantic definitions.
  • Serve as the enterprise authority on semantic engineering and ontology practices.
  • Contribute to communities of practice, reference guidance, and internal enablement materials.

Skills & Experience
  • 8-12+ years of hands-on experience in semantic layer architecture, ontology modeling, and knowledge graph design at enterprise scale.
  • Deep, hands-on expertise with RDF, OWL (OWL2), RDFS, SKOS, SPARQL (querying, optimization, semantic analytics), and W3C semantic web standards
  • Proven experience designing and operating knowledge graphs at enterprise scale
  • Hands-on experience with graph or triple-store technologies (e.g., Neo4j, Neptune, TigerGraph, Spanner Graph)
  • Experience integrating knowledge graphs with LLMs, RAG pipelines, vector stores, and Agentic frameworks.
  • Strong understanding of AI consumption patterns, including embeddings, grounding, and explainability
  • Experience integrating semantic layers with data platforms, APIs, metadata systems, and AI pipelines
  • Ability to translate complex domain knowledge into formal, machine-readable semantic structures
  • Strong understanding of context-aware data engineering and semantic interoperability.
  • Proven ability to move from strategy → pilot → scaled enterprise capability.
  • Strong executive influence and thought leadership in Agentic analytics and AI-native data engineering.
  • Hands-on experience with AWS, GCP, and Snowflake
  • Excellent communication, presentation, and leadership skills.

Education, Certifications and Licenses
  • Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent work experience.

Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$156,000 - $234,000
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
About Us | Our Culture | What It's Like to Work Here | Perks & Benefits

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About Hartford

Sourced by ZipRecruiter

Hartford Financial Services Group, widely recognized as The Hartford, is a renowned company based in Hartford, CT, US. Established in 1810, it has evolved into an industry leader in the insurance and financial services sector, proudly serving more than one million businesses in the US. The Hartford is committed to offering a gamut of insurance products that include homeowners, automobile, and business insurance as well as employee benefits and mutual funds. The company’s core values revolve around customer-focused innovations, diversity and inclusion, and ethical dealings that have earned them a customer-centric reputation. This shapes their mission which revolves around aiding their clients to overcome unforeseen obstacles and enhancing their wealth over time. Among the company's noted accomplishments is being consistently listed among the World's Most Ethical Companies, a testament to their unwavering commitment towards responsible business practices.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

Hartford, CT, US

Year founded

1810

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