Director of Knowledge Graphs
Career GuideKey Responsibilities
- Set the vision and roadmap for knowledge graph capabilities and business outcomes
- Partner with product, engineering, data, and legal teams to prioritize use cases
- Design the core data model for entities, relationships, and key attributes
- Define standards for data quality, governance, and responsible data use
- Oversee data ingestion pipelines and ongoing data refresh processes
- Guide approaches for entity resolution and record linking across sources
- Establish metrics for impact such as search relevance, coverage, and latency
- Lead build versus buy decisions for graph databases and supporting tools
- Manage a team of engineers and scientists and hire for critical roles
- Communicate progress and tradeoffs to executive stakeholders
Top Skills for Success
Technical Leadership
Stakeholder Management
Product Strategy
Data Modeling
Graph Data Modeling
Ontology Design
Data Governance
Data Quality Management
Entity Resolution
Information Retrieval
Machine Learning Foundations
Cloud Architecture
Career Progression
Can Lead To
Head of Knowledge Graph
Director of Data Platform
Director of Data Engineering
Director of Data Products
Director of AI Platform
Transition Opportunities
Vice President of Data
Vice President of Engineering
Chief Data Officer
Head of Search
Head of AI Products
Common Skill Gaps
Often Missing Skills
Ontology DesignEntity ResolutionGraph Query LanguagesData GovernanceSearch Relevance MeasurementData Product ManagementCost ManagementSecurity and Privacy
Development SuggestionsBuild one end to end graph product as a portfolio project, define clear success metrics, and document decisions. Strengthen governance skills by creating a lightweight model review process, data quality rules, and access controls. Practice executive communication by writing one page strategy updates tied to measurable outcomes.
Salary & Demand
Median Salary Range
Entry LevelRare at true entry level. Typical starting level for this title begins around USD 180,000 to 230,000 base in the US market
Mid LevelUSD 200,000 to 260,000 base, often with bonus and equity depending on company stage
Senior LevelUSD 240,000 to 330,000 base, with total compensation commonly higher in large tech and well funded AI companies
Growth Trend
Growing demand, driven by AI search, enterprise data integration, personalization, and governance needs. Hiring is strongest in tech, finance, healthcare, and data platform teams.Companies Hiring
Major Employers
GoogleMicrosoftAmazonMetaAppleNetflixSalesforceSnowflakeDatabricksPalantirBloombergJPMorgan Chase
Industry Sectors
Consumer technologyEnterprise softwareFinancial servicesHealthcareEcommerceMedia and entertainmentTelecommunicationsPublic sector
Recommended Next Steps
1
Clarify your target use cases such as search, recommendations, analytics, or AI assistants2
Create a sample ontology and entity model for a real domain and publish it as a case study3
Lead a pilot that connects at least three data sources and demonstrates measurable lift4
Build a hiring plan covering graph engineering, data engineering, and data science needs5
Develop a governance playbook with ownership, quality checks, and review cadence6
Strengthen your executive narrative with a roadmap, risks, and expected impact