Knowledge Graph Engineer
Impact: Enterprise knowledge management and AI reasoning quality through structured data
Build and maintain knowledge graphs that represent structured relationships between entities for reasoning, search, and AI grounding applications. Design ontologies, entity resolution pipelines, and graph query systems that power enterprise knowledge management.
What the day looks like
- People interaction
- Minimal
- Team vs solo
- 45% Team / 55% Solo
- Client facing
- Sometimes
- Impact visibility
- Moderate
- Travel
- Minimal
- Schedule flexibility
- Flexible
- Remote work
- Mostly Remote
- Typical work hours
- 40 to 50 hours/week
- Stress level
- Moderate
At a glance
- Median salary
- $150,000
- Entry-level
- $105,000 - $130,000
- Senior
- $200,000+
- Growth by 2033
- 20% (faster than average)
- Demand
- Growing
- Freelance potential
- Low
- Salary growth potential
- High - 65 to 80% growth from entry to senior
- Typical student debt
- $30,000 - $80,000
Skills you'll use
Hard skills
- Neo4j
- SPARQL
- Python
- RDF/OWL
- Entity resolution
- Graph neural networks
- Cypher
Soft skills
- Analytical thinking
- Attention to detail
- Systems thinking
- Intellectual curiosity
- Problem-solving
Technical complexity: Very High
How to get there
- Minimum education
- Master's Degree
- Licensing
- No
- Years to mid-career
- 3 to 5 years
- Years to senior
- 7 to 10 years
- Career switching
- Hard
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- Data Engineer > Knowledge Graph Engineer > Senior KG Engineer > Staff Knowledge Engineer > Principal Engineer
Future outlook
- Automation probability
- 15% low risk as ontology design and entity resolution require domain expertise
- AI disruption risk
- Moderate
- Demand trend
- Growing
How people feel about it
- Overall satisfaction
- 7.7/10
- Meaning
- 7.9/10
- Work-life balance
- 7.2/10
- Prestige
- 7.7/10
- Social perception
- Moderate