Director of Knowledge Graphs

Career Guide

A Director of Knowledge Graphs leads the strategy, design, and delivery of graph based data products that connect people, places, things, and concepts. The role sits at the intersection of data engineering, data science, and product leadership, with a focus on making data easier to find, trust, and use for search, recommendations, analytics, and AI applications.

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Key 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

Clarify your target use cases such as search, recommendations, analytics, or AI assistants

Create a sample ontology and entity model for a real domain and publish it as a case study

Lead a pilot that connects at least three data sources and demonstrates measurable lift

Build a hiring plan covering graph engineering, data engineering, and data science needs

Develop a governance playbook with ownership, quality checks, and review cadence

Strengthen your executive narrative with a roadmap, risks, and expected impact