Knowledge Engineer / Knowledge Graph Engineer
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Model enterprise meaning explicitly and query connected knowledge
- Turn fragmented data into curated, production-grade semantic assets
- Ground AI and agents in governed, high-quality enterprise knowledge
Benchmark: Accenture Knowledge Engineer + Senior Knowledge Engineer (current Sep 2026) Market refresh: ontologies, semantic layers, RDF/OWL/SPARQL/SHACL, graph databases, entity resolution, graph pipelines, semantic search, GraphRAG, governed LLM/agent grounding and reusable enterprise knowledge services.
Search Live Openings
We don’t have a direct job-board feed matched to this one yet, so here are real, live US job searches for “Knowledge Engineer / Knowledge Graph Engineer” instead.
Model enterprise meaning explicitly and query connected knowledge
Ontologies, taxonomies + semantic modeling
Coverage: Knowledge Graphs / Semantic Modeling / Data Modeling
RDF / SPARQL + graph query fundamentals
Coverage: Knowledge Graphs / Graph Databases / Query Languages
Turn fragmented data into curated, production-grade semantic assets
Entity extraction, resolution + graph ingestion
Coverage: Data Engineering / Entity Resolution / Data Quality
Semantic layers, metadata + graph architecture
Coverage: Metadata / Data Governance / Graph Platforms
Ground AI and agents in governed, high-quality enterprise knowledge
GraphRAG, hybrid retrieval + agent grounding
Coverage: GraphRAG / RAG / Search & Retrieval / AI Agents
Knowledge governance, validation + lifecycle
Coverage: Data Governance / Quality / Lineage / AI Governance
Role-Readiness Proof / Capstone
Build a governed enterprise knowledge service from raw structured and unstructured sources. Define a domain ontology and taxonomy; map entities and relationships; ingest into a graph database; implement RDF/SPARQL or equivalent graph queries; add entity resolution, validation and provenance; expose semantic search and GraphRAG to an agent; then measure retrieval quality, grounding, freshness and graph quality under realistic change.
Proof Rubric
- Prove ontology and schema quality
- correct entity resolution
- repeatable graph ingestion
- query correctness
- validation and provenance
- retrieval and GraphRAG relevance
- grounded agent answers with citations
- security and governed access
- change/version handling
- observability and measurable quality. Explain when a graph is superior to relational, vector-only or document-only approaches