Knowledge Graph Engineer Job Description

A Knowledge Graph Engineer builds and maintains the graph that tells software what things are and how they relate to each other. A relational database stores rows in tables. A knowledge graph stores entities (a customer, a drug compound, a supplier, an insurance policy) as nodes, stores the relationships between them as typed edges, and attaches a formal vocabulary that defines what every type and relationship means. The engineer designs that vocabulary, loads data into it, keeps it consistent, and exposes it to the applications and AI systems that query it.

The work runs in three layers.

Modeling. The engineer writes the ontology or schema: the classes, properties and constraints that describe the domain. On the W3C stack that means RDF for the data itself, RDFS or OWL for class hierarchies and property definitions, and SHACL shapes for validation rules such as "every Policy has exactly one effective date and one named insured." On a property-graph stack (Neo4j, Amazon Neptune, TigerGraph, Memgraph) it means a model of node labels, relationship types and properties, enforced with database constraints and documented for every team that writes to it. Ontology editors like Protégé, shared vocabularies like schema.org, and domain standards such as FIBO in finance or SNOMED CT in clinical data can serve as starting points, but someone still has to decide how the company's own concepts map onto them.

Population and identity. Data arrives from relational systems, APIs, PDFs and spreadsheets. The engineer writes the mappings and ingestion jobs (R2RML or RML mappings, Python with rdflib or the Neo4j driver, Spark jobs, a graph database's bulk loader) that turn that data into nodes and edges. Then comes entity resolution: deciding that "Acme Corp.", "ACME Corporation" and a vendor record sharing the same tax ID are one organization, merging them under a single stable identifier, and recording which sources and which evidence justified the merge. Identity errors are costly, because every downstream answer inherits them.

Serving. Applications ask the graph questions in SPARQL, Cypher or Gremlin. The engineer writes and tunes those queries, builds APIs and query templates on top of them, and runs graph algorithms such as community detection, shortest path or centrality when a product needs them. For AI products, the engineer connects the graph to retrieval: an LLM application asks for the entities and relationships around a user's question, and the graph returns facts with their provenance so the model can cite them. That boundary matters. The knowledge graph engineer owns the facts, the identifiers and the meaning of each relationship. Chunking documents, choosing embedding models and writing prompt templates belong to whoever owns the retrieval pipeline.

A normal week mixes all three. It might include reviewing a pull request that adds new classes to the ontology, running SHACL validation after a nightly load and tracing violations back to the source system that produced them, sitting with underwriters, chemists or intelligence analysts to pin down what a term actually means, rewriting a slow multi-hop query, and updating the data dictionary so other teams can find what they need.

The people around this role are data engineers who own upstream pipelines, machine learning and AI engineers who consume the graph, domain experts who define the concepts, data governance and taxonomy staff, and product managers who decide which questions the graph has to answer first. Deliverables are concrete: ontology files under version control, mapping specifications, validation reports, entity-resolution rules with match-quality measurements, query endpoints, and documentation.

As a rough guide, the starting point is a bachelor's degree in computer science, information science, data science or a domain field (biology, chemistry, finance) paired with real programming skill. O*NET places Database Architects, the occupation this page uses for pay, in Job Zone Four and notes that "most of these occupations require a four-year bachelor's degree, but some do not." The BLS Occupational Outlook Handbook lists a bachelor's degree as the typical entry-level education for database administrators and architects, plus work experience in a related occupation.

Backgrounds that feed the role vary. Library and information science graduates bring taxonomy and controlled-vocabulary discipline. Philosophy and linguistics graduates who learned formal logic and Python can be strong ontology modelers. Data engineers and database developers bring the loading, performance and operations side. A master's or PhD in knowledge representation, semantic web research or natural language processing helps for research-heavy teams, but it is not the only route. As a rough guide, three to five years in data engineering, database development or ontology work comes before a senior title.

The technical core to learn:

• Semantic web standards: RDF, RDFS, OWL, SHACL and SPARQL. SPARQL 1.1, published as a W3C Recommendation in 2013, is still the current Recommendation. The latest SPARQL 1.2 Query Language Working Draft is dated Nov. 1, 2026, and RDF 1.2 Concepts and Abstract Data Model was published as a Candidate Recommendation Snapshot on April 7, 2026, so engineers should track both specifications without assuming every triple store supports them.
• Property graphs: Cypher or openCypher, Gremlin, and the data-modeling habits of Neo4j or Amazon Neptune.
• Programming: Python (rdflib, pandas, PySpark), SQL, and enough Java or Scala to work inside JVM-based triple stores.
• Entity resolution: blocking, pairwise matching, clustering and the evaluation of precision and recall on a labeled sample.
• Graph algorithms and graph machine learning: community detection, centrality, link prediction and node embeddings.
• LLM integration: prompting a model to extract entities against a fixed schema, and checking its output against SHACL shapes or constraint queries before anything is loaded.

Certifications are optional and vendor-specific. Candidates working on Neo4j can earn the Neo4j Certified Professional and Neo4j Graph Data Science Certification, both awarded by Neo4j. On AWS-heavy teams, the AWS Certified Data Engineer - Associate from Amazon Web Services covers the surrounding pipeline work.

A portfolio gives a hiring manager something concrete to review. A public ontology with SHACL shapes, a small knowledge graph built from open government data with documented entity-resolution rules, or a write-up of how a messy merge problem was solved shows how a candidate makes modeling decisions and defends them.

Knowledge graphs are older than the current AI wave. Google introduced its Knowledge Graph on May 16, 2012, under the headline "things, not strings," saying it then contained more than 500 million objects and more than 3.5 billion facts about and relationships between them.

What changed is how graphs get built and who consumes them. Microsoft Research described GraphRAG on Feb. 13, 2024, an approach that uses an LLM to create a knowledge graph from a private dataset and then uses that graph to augment prompts at query time. Microsoft released the GraphRAG code on GitHub on July 2, 2024. A graph extracted by a model still needs a schema to extract against and merge rules applied afterward, and that schema and identity work is what this role owns.

BLS does not report knowledge graph engineers separately. The closest occupation, Database Architects, employed 67,140 people in May 2025. The Occupational Outlook Handbook states that "as organizations continue to improve their systems and adopt artificial intelligence (AI) to process their data, database architects will be critical to ensuring proper database design, transition, backup, and security." BLS projects employment of database administrators and architects combined to grow 4 percent from 2025 to 2035, about as fast as the average for all occupations, with about 7,300 openings a year on average. Looking at database architects alone, O*NET flags the occupation as a Bright Outlook occupation, rates its projected growth for 2025 to 2035 as "much faster than average (7% or higher)," and projects about 3,900 openings a year on average. The two figures cover different groups: the BLS rate combines database administrators with architects.

Industry shapes pay inside that occupation. In May 2025, database architects in computer systems design and related services earned a median of $148,680, with 16,410 employed in that industry. In finance and insurance, the median was $146,470 with 9,370 employed. BLS does not break out the graph specialists within those counts.

Career paths branch in a few directions. Some engineers move up to principal or staff knowledge graph engineer and own the enterprise ontology across many products. Some move into data architecture and set modeling standards for relational, graph and vector stores together. Others move toward AI platform roles, where the graph becomes one retrieval source among several and the job widens to evaluation and governance of what AI systems are allowed to assert.

For job seekers, the practical move is to build evidence of judgment rather than a list of tools. A documented ontology with its design decisions explained, an entity-resolution project with measured precision and recall on a labeled sample, and an example of a graph-backed AI answer that cites the source of each fact all show the parts of the job that a cloud extraction feature does not do on its own: deciding what the domain's concepts are, which records describe the same thing, and why the graph says what it says. Those are the questions a domain expert will ask.

Dear Hiring Manager,

I am applying for the Knowledge Graph Engineer position on your data platform team. For the past four years I have built and run the product knowledge graph at a mid-sized industrial parts distributor, and the work you describe, connecting the graph to your new AI assistant while keeping its facts traceable, is exactly the problem I want to spend the next several years on.

When I joined, our product data lived in three systems that disagreed about what a "part" was. I wrote the first version of our ontology in OWL, with SHACL shapes for every class that downstream teams depended on, and kept it in Git so changes went through review like any other code. I built the RML mappings and Python loaders that move data from our ERP and supplier feeds into GraphDB each night, and I own the validation report that goes to the data stewards every morning.

The project I am proudest of is our supplier entity-resolution pipeline. We had the same manufacturers listed under many spellings and several tax identifiers. I designed the blocking and matching rules, built a labeled sample with our procurement team to measure precision and recall, and set up a review queue so that low-confidence merges went to a person instead of straight into the graph. Every merge now carries its evidence and source, which made the procurement team trust the graph enough to use it for contract negotiations.

Last year I worked with our machine learning engineers on a pilot that let a language model answer catalog questions using the graph. My part was the query templates and the provenance fields that let the assistant cite where each fact came from. I learned a lot about where model extraction helps and where it quietly invents relationships that the schema should have blocked.

I would welcome the chance to talk about your ontology roadmap and how I could help.

Sincerely, Jordan Ellis