Head of Knowledge Graphs

Career Guide
A Head of Knowledge Graphs leads the strategy, people, and delivery of knowledge graph systems that connect data into a shared, trusted representation of concepts and relationships. This role typically sits at the intersection of data engineering, applied machine learning, search, and product, turning messy information into structured knowledge that powers discovery, recommendations, analytics, and automation.

Key Responsibilities

  • Set the vision and roadmap for knowledge graph capabilities aligned to business goals
  • Define standards for entity definitions, relationship rules, and data quality
  • Lead teams across data engineering, applied machine learning, and platform engineering
  • Oversee data ingestion pipelines and integration from internal and external sources
  • Design governance processes for ownership, stewardship, and change management
  • Partner with product leaders to turn use cases into deliverable features
  • Establish metrics for accuracy, freshness, coverage, and business impact
  • Select and manage graph databases and supporting tooling
  • Ensure privacy, security, and responsible data use across the graph
  • Communicate progress and tradeoffs to executives and cross functional stakeholders

Top Skills for Success

Knowledge Graph Strategy
Graph Data Modeling
Ontology Design
Entity Resolution
Data Governance
Data Quality Management
Data Integration
Graph Databases
Applied Machine Learning
Information Retrieval
Search Relevance
Product Management
Executive Communication
Stakeholder Management
People Leadership

Career Progression

Can Lead To
Director of Data Engineering
Director of Machine Learning
Head of Data Platform
Head of Search
Head of Data Governance
Transition Opportunities
Vice President of Data
Vice President of Engineering
Chief Data Officer
Head of AI Platform
General Manager of Data Products

Common Skill Gaps

Often Missing Skills
Ontology DesignEntity ResolutionData GovernanceGraph DatabasesSearch RelevanceInformation RetrievalMetric DesignChange Management
Development SuggestionsBuild a portfolio of two to three production style graph use cases, such as catalog normalization, customer identity graph, or enterprise knowledge search. Practice defining clear entity standards, quality metrics, and ownership. Strengthen collaboration skills by running cross functional workshops to align definitions and resolve conflicts early.

Salary & Demand

Median Salary Range
Entry LevelRare as an entry role, typical total pay in the United States is 180,000 to 250,000 USD
Mid LevelTypical total pay in the United States is 230,000 to 350,000 USD
Senior LevelTypical total pay in the United States is 320,000 to 550,000 USD, higher in top tier technology firms
Growth Trend
Strong and rising, driven by search relevance, recommendations, data integration, and adoption of retrieval based AI systems

Companies Hiring

Major Employers
GoogleMicrosoftAmazonAppleMetaOpenAIAnthropicNetflixUberAirbnbSalesforceServiceNowLinkedInBloombergWalmart
Industry Sectors
TechnologyEnterprise SoftwareEcommerceMedia and StreamingFinancial ServicesHealthcareRetailTravelLogisticsPublic Sector

Recommended Next Steps

1
Create a one page knowledge graph strategy linked to three business outcomes
2
Inventory existing data sources and define the top ten entities and relationships
3
Design a quality scorecard covering accuracy, completeness, and freshness
4
Choose a pilot use case with a clear baseline and measurable lift
5
Establish a governance model with named owners and review cadence
6
Build an executive ready roadmap with milestones and resourcing needs
7
Prepare interview stories that show impact, tradeoffs, and team leadership