Interview scorecard template

Enterprise Knowledge Graph Specialist interview scorecard

Evaluate Enterprise Knowledge Graph Specialist candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check fluency in RDF/OWL or property graphs: ask them to walk through a SPARQL or Cypher query they tuned, SHACL shapes they. Use the rubric to compare role-specific evidence consistently.

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software dataentity resolutionontology modellingrdf sparqlshacl validation
TL;DR
For technical proficiency, look for evidence the candidate names triple stores used (Stardog, GraphDB, Neptune, Neo4j), writes non-trivial SPARQL from memory, and explains OWL inference limits precisely. For systems and trade-offs, look for evidence the candidate justifies modelling decisions against query patterns, ingest volume and governance cost, and admits where a chosen schema later needed refactoring. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Technical proficiency

35% weight

Check fluency in RDF/OWL or property graphs: ask them to walk through a SPARQL or Cypher query they tuned, SHACL shapes they authored, and reasoner behaviour they relied on.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Names triple stores used (Stardog, GraphDB, Neptune, Neo4j), writes non-trivial SPARQL from memory, and explains OWL inference limits precisely.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe ontology design choices: reuse of SKOS, schema.org or industry models like FIBO versus bespoke classes, and when they chose LPG over RDF for a workload.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Justifies modelling decisions against query patterns, ingest volume and governance cost, and admits where a chosen schema later needed refactoring.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they proved graph quality: entity resolution precision on customer or product records, SHACL validation coverage, competency questions, and drift monitoring after each ingest run.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Quotes measured match rates, constraint violation counts and query latency, and describes the validation harness that caught bad loads before publication.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they extracted meaning from domain experts and sold the graph internally: workshops with data stewards, feeding search or GraphRAG teams, stewardship handover.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Describes running competency question sessions with business SMEs and converting vague vocabulary into agreed, documented, versioned ontology terms.

Evidence-led prompts

Interview questions for a Enterprise Knowledge Graph Specialist

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Describe your experience building and running large knowledge graphs.

  2. 02

    Can you give an example of linking disparate data sources into one graph?

  3. 03

    Describe your experience with graph database technologies.

  4. 04

    Explain your process for identifying and representing an ontology for a domain.

  5. 05

    Can you explain your familiarity with the relevant semantic standards?

See the complete Enterprise Knowledge Graph Specialist question set
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