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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.

Data Scientist (Knowledge Graph & Identity)

Samba

San Francisco, CA • On-site

Full-time

Posted 10 days ago

Job description

Samba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant. 

As a mid-level Data Scientist on Samba's Knowledge Graph & Identity team in Warsaw, you will own end-to-end delivery of significant data science projects with minimal guidance. You are a reliable, autonomous contributor with deep expertise in at least one of Samba's core domains - knowledge graphs, identity spine, measurement, or audience modeling - and the technical range to build production-ready solutions using modern ML and AI methodologies. You'll work closely with peers, product, and engineering, and play an active role in mentoring junior data scientists on the team.
What You'll Do:
  • Own end-to-end delivery of significant data science projects — from problem scoping and approach design through to production deployment, with a focus on knowledge graph and identity solutions
  • Make sound, independently-reasoned decisions on methodology, model selection, and evaluation; document them clearly in technical solution documents covering problem statement, approach, metrics, and timeline
  • Lead solution design for your own initiatives; break down complex epics into well-scoped user stories with clear acceptance criteria, adopting DataOps and MLOps best practices throughout — experiment tracking, pipeline orchestration, model monitoring, and reproducibility
  • Build production-quality Python and PySpark code on Databricks — well-tested, documented, and reusable — and implement advanced ML and AI-powered workflows including entity resolution, probabilistic record linkage, embedding-based matching, semantic similarity, and LLM-augmented pipelines
  • Develop and maintain reusable tools, libraries, and documentation that improve team efficiency and technical standards; conduct code reviews with constructive, specific feedback that raises the bar
  • Mentor junior data scientists on technical execution, code quality, and career development; lead internal talks or workshops on knowledge graphs, identity, or ML topics
  • Collaborate cross-functionally with product, engineering, and operations — translate business requirements into technical specifications, partner with data engineering on scalable pipeline design, and participate in cross-functional design reviews and working groups
Who You Are:
  • Bachelor's degree required in Statistics, Data Science, Computer Science, Mathematics or a related quantitative field; Master's strongly preferred
  • 3–5 years of hands-on data science experience with demonstrated ability to own and deliver complex, multi-sprint projects independently
  • Advanced Python with production-quality code, testing, and documentation; strong SQL and PySpark for billion-row datasets
  • Databricks workflows, Delta Lake, and job orchestration; working knowledge of cloud platforms (AWS or GCP)
  • Solid command of core ML — regression, classification, clustering, model evaluation, and experimental design — applied to complex, high-volume data
  • Proficiency with MLOps practices: experiment tracking, pipeline orchestration (Airflow), and reproducible model deployment
  • Exposure to modern AI methodologies: RAG systems, LLM-augmented models, vector databases, and semantic search
  • Strong communicator — able to translate technical work into clear documentation, user stories, and cross-functional conversations
  • Demonstrated ability to mentor junior data scientists and contribute to team standards
Preferred skills:
  • Hands-on experience with knowledge graph construction, entity resolution, or semantic data modeling (RDF, OWL, SPARQL, or equivalent graph frameworks)
  • Familiarity with probabilistic record linkage, identity graph approaches, or embedding-based entity matching at scale
  • Experience with causal inference methods (A/B testing, synthetic control, uplift modeling)
  • Experience with deduplication, enrichment, or web-to-TV linkage problems
  • Background in media, ad tech, or measurement — TV viewership (ACR/STB data), digital audience modeling, cross-platform measurement (linear + CTV/OTT), or identity resolution in privacy-constrained environments
  • Familiarity with the measurement and identity vendor landscape (Nielsen, Comscore, LiveRamp, The Trade Desk
Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.  We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.
 
Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.