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Knowledge Graph Rdf Jobs (NOW HIRING)

Squirro

Knowledge Graph Engineer

New York, NY · Remote

In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL) * Familiarity with graph databases, particularly RDF graph databases * Familiarity with Large Language Models (LLMs) and ...

Squirro

Knowledge Graph Engineer

New York, NY

In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL) * Familiarity with graph databases, particularly RDF graph databases * Familiarity with Large Language Models (LLMs) and ...

Squirro

Knowledge Graph Engineer

New York, NY · On-site

In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL) * Familiarity with graph databases, particularly RDF graph databases * Familiarity with Large Language Models (LLMs) and ...

Squirro

Knowledge Graph Engineer

Manhattan, NY · On-site

In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL) * Familiarity with graph databases, particularly RDF graph databases * Familiarity with Large Language Models (LLMs) and ...

Peraton

Knowledge Graph/Ontology Engineer

Basking Ridge, NJ · On-site

$104K - $166K/yr

Proficiency with RDF, OWL, SPARQL, and semantic web technologies * Experience with graph databases ... Experience with entity resolution and knowledge base population * Familiarity with linked data ...

Peraton

$104K - $166K/yr

Knowledge Graph Engineer Peraton Labs is seeking a knowledge graph engineer to extend and maintain ... Proficiency with RDF, OWL, SPARQL, and semantic web technologies * Experience with graph databases ...

Peraton

Knowledge Graph/Ontology Engineer

Basking Ridge, NJ · On-site

$104K - $166K/yr

Knowledge Graph/Ontology Engineer Job Locations US-MD-Annapolis Junction | US-NJ-Basking Ridge ... Proficiency with RDF, OWL, SPARQL, and semantic web technologies * Experience with graph databases ...

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How much do knowledge graph rdf jobs pay per hour?

As of Jun 2, 2026, the average hourly pay for knowledge graph rdf in the United States is $33.84, according to ZipRecruiter salary data. Most workers in this role earn between $21.88 and $44.95 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Knowledge Graph RDF Specialist, and why are they important?

A Knowledge Graph RDF Specialist needs a solid understanding of semantic web concepts, RDF (Resource Description Framework), SPARQL querying, and experience with data modeling, often backed by a degree in computer science or information science. Familiarity with graph databases (such as Neo4j or GraphDB), ontology management tools, and standards like OWL or SKOS is typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate complex data relationships into actionable insights. These skills ensure accurate knowledge representation, interoperability, and the ability to derive meaningful connections from large, diverse datasets.

What are the typical challenges faced when maintaining and updating a Knowledge Graph using RDF in a dynamic business environment?

Maintaining and updating a Knowledge Graph built on RDF can be challenging due to the need to ensure data consistency as new sources are integrated or existing data evolves. Keeping ontologies current and aligned with changing business requirements requires collaboration with domain experts and regular reviews. Additionally, handling large volumes of data efficiently while ensuring query performance and interoperability with other systems is an ongoing task. Effective communication with data engineers, analysts, and business stakeholders is essential to address these challenges and deliver reliable, business-relevant knowledge graphs.

What is a Knowledge Graph RDF specialist?

A Knowledge Graph RDF specialist is a professional who designs, builds, and maintains knowledge graphs using the Resource Description Framework (RDF). RDF is a standard model for data interchange on the web, allowing information to be structured as interconnected entities and relationships. These specialists work with semantic web technologies to organize complex data, enable advanced search and reasoning, and support machine learning applications. They often collaborate with data engineers, ontologists, and software developers to implement scalable and interoperable data solutions.

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

AspectKnowledge Graph RdfData Scientist
Required credentialsKnowledge of RDF, SPARQL, ontologiesStatistics, programming, domain expertise
Work environmentSemantic web, data integration, knowledge managementData analysis, modeling, predictive analytics
Employer and industry usageTech companies, research institutions, semantic web projectsFinance, healthcare, tech firms, consulting
Common search and comparison intentUnderstanding semantic data modelingAnalyzing data for insights

Knowledge Graph Rdf specialists focus on creating and managing semantic web data using RDF and related technologies, while Data Scientists analyze data to extract insights. Both roles require technical skills but serve different purposes within data management and analysis.

Infographic showing various Knowledge Graph Rdf job openings in the United States as of May 2026, with employment types broken down into 33% Temporary, and 67% Contract. Highlights an 54% Physical, 4% Hybrid, and 42% Remote job distribution, with an average salary of $70,381 per year, or $33.8 per hour.

Knowledge Graph Engineer

Squirro

New York, NY • Remote

Full-time

Posted 21 days ago

Job description

Salary: USD 100'000-150'000

We are looking for a Knowledge Graph Engineer to join our team in the US, supporting the Delivery team in designing, implementing, and deploying knowledge graphdriven SaaS solutions for enterprise customers.


What Youll Do

As a Knowledge Graph Engineer, you will work closely with customers and internal product and engineering teams to design and deploy semantic and knowledge graph solutions that deliver measurable business value.


  • Client Engagement & Solution Delivery: Manage client engagements from pre-sales through onboarding, deployment, and training, ensuring successful adoption of knowledge graphbased solutions.
  • Semantic Modeling & Knowledge Graph Design: Develop and maintain taxonomies, ontologies, and classification models. Design semantic structures that align user needs with business objectives.
  • Cross-functional Collaboration: Collaborate with product, engineering, and delivery teams to translate customer requirements into scalable semantic and technical solutions.
  • Product Contribution & Customer Feedback: Gather and prioritize client requirements, contribute ideas to the product roadmap, and support product positioning through demos, content, and customer-facing activities.


What You Bring

You will support the Product and Delivery functions in building and deploying high-quality semantic solutions.


  • Strong Python experience
  • Experience in customer-facing roles within software or SaaS environments
  • Strong communication and presentation skills
  • Practical experience developing taxonomies, ontologies, and knowledge graphs
  • Experience managing human and/or machine classification
  • In-depth knowledge of semantic web standards (RDF, SKOS, OWL, SPARQL)
  • Familiarity with graph databases, particularly RDF graph databases
  • Familiarity with Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)
  • Ability to collaborate effectively across technical and non-technical teams


What we offer


  • Drive growth in a dynamic, high-potential tech environment
  • Enjoy autonomy with our "freedom and responsibility" work approach
  • Collaborate with exceptional talent solving extraordinary challenges
  • Drive customer success and retention while enjoying a flexible environment focused on growth
  • Make a strategic impact while having fun with awesome colleagues


About Squirro

Squirro is the enterprise AI platform built for regulated industries, streamlining enterprise search and automating complex, custom workflows. Secure, private, scalable, permissions-aware, and fully auditable, the platform ensures that every result is accurate and verifiable. Squirro powers agentic AI applications that are grounded in the organizations unique enterprise ontology.

Further information about AI-driven business insights can be found at:
https://squirro.com/