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Ontology Engineer
Also called Semantic Ontologist
An Ontology Engineer defines the concepts, relationships and rules that describe a business domain, so retrieval and AI systems return answers that are consistent, checkable and trustworthy.
What they do
They sit with domain experts and work out what the things in the domain are, how they relate and what must always or never be true. They model those ideas as classes, relations and constraints in RDF and OWL, using ontology editors. They map messy source data, often in SQL tables, onto the model. They write competency questions, which are the plain-language questions the ontology must be able to answer, and turn them into tests. They run reasoners and validation checks, fix inconsistencies, and keep each release versioned with clear change notes.
What a strong hire delivers
In the first months, a strong hire agrees a scoped model of the core domain with the people who know it. They ship a first versioned ontology with a written set of competency questions and tests that run on every change. They map at least one real data source into it, record where each fact came from, and show the team which questions can now be answered reliably and which cannot yet.
How you can tell they are good
Ask them to walk through an ontology they built and explain what they left out and why. Good candidates show competency questions, tests and version history, not just diagrams. Ask how they handled two experts who disagreed. Listen for restraint, clear naming, and awareness of how a model is used in retrieval. Be wary of over-modelling and of people who cannot say how they knew it worked.
Why it is hard to fill
Few people carry this title, so searches by title return little. The skill sits across knowledge engineering, library and information science, databases and semantic web research. Many strong people are academics or consultants who have not worked in a product team. Others know graph databases but not formal modelling. Candidates who can do both the rigorous modelling and the stakeholder work are rare, and they are often not looking publicly.
Where to find them
Look in knowledge engineering teams, information science and library backgrounds, and data modelling or data governance roles in large organisations. Publishing, healthcare, life sciences, finance and legal data teams all employ people who do this work under other names, such as taxonomist, semantic architect or knowledge graph engineer. Research groups working on the semantic web are another source, as are consultancies that build enterprise knowledge graphs.
Indicative pay
Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.
Interview questions for an Ontology Engineer
- Tell me about an ontology you built. What problem was it solving and how did you decide its scope?
A strong answer names a real use, the users, and what was deliberately excluded. They tie scope to questions the model had to answer. Worry if they describe only the technology, or a model that tried to cover everything.
- How do you write competency questions, and how do you turn them into tests?
Look for questions gathered from domain experts, phrased in plain language, then expressed as queries with expected results. They should run automatically on each change. Worry if testing means eyeballing a diagram or a one-off review.
4 more questions, with what a strong answer covers, are in the Role Kit.
Job description you can post
We are building retrieval and AI features that depend on a clear, shared model of our domain. We need an Ontology Engineer to define the concepts, relationships and constraints that make our answers consistent and trustworthy, and to keep that model tested and versioned as the product grows.
What you will do
- Work with domain experts to define concepts, relations and constraints
- Build and maintain a versioned ontology in RDF and OWL
- Write competency questions and automate them as tests
- Map source data, including SQL databases, onto the model
- Run validation and reasoning checks and resolve inconsistencies
- Record provenance and data quality rules for key facts
- Document changes and support teams who use the ontology
What you will bring
- Ontology or knowledge model you have designed and can walk us through
- Working knowledge of RDF, OWL and ontology editors
- Experience writing competency questions or equivalent tests
- Confident with SQL and mapping messy data to a model
- Experience with graph databases and query languages
- Clear communication with non-technical domain experts
Nice to have
- Experience supporting retrieval or search systems
- Background in information science or knowledge engineering
- Experience in a regulated or data-heavy domain
Free to use and adapt. If it saves you time, a link back to this page is appreciated.
Skills that matter
- Knowledge representationAdvancedRepresent facts, relations and constraints explicitly
- Information retrievalAdvancedRetrieve and rank relevant documents or entities
- Data quality and provenanceWorkingTrack dataset origin, validity and transformations
- SQLWorkingQuery and validate relational data
Other roles in knowledge graphs and semantic systems
Related roles in other families
How we wrote this
Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.
- Computer and Information Research Scientists, 15-1221.00, O*NET / US Department of Labor
- Transformers documentation, Hugging Face
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Role Kit
Ontology Engineer: the Role Kit
- What the role is and what a strong hire delivers
- Every interview question, with what a strong answer covers
- A ready-to-post job description
- Where to find the people, and the other titles they use
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