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 hands-on command of RDF/OWL, SHACL shapes, SPARQL or Cypher, and triple stores like GraphDB or Neo4j, plus embedding models and chunking strategies used in retrieval pipelines.
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 specific ontologies authored or extended, writes non-trivial SPARQL from memory, and explains SHACL validation versus OWL inference precisely.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe design choices: property graph versus RDF, taxonomy depth, entity resolution thresholds, when to use graph retrieval over pure vector search, and index refresh cadence.
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 and ingestion cost, and admits where a simpler flat schema would have served better.
03
Evaluation factor
Evidence and rigour
25% weight
Test how they measured retrieval quality: gold question sets, recall@k, groundedness or hallucination rates, SME review loops, and regression checks after ontology changes.
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
Cites baseline and post-change numbers on a named evaluation set, and describes catching a regression before it reached users.
04
Evaluation factor
Collaboration and communication
15% weight
Assess elicitation work with subject matter experts: competency questions, terminology disputes, curation workflows, and handing schemas to application engineers or data stewards.
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 workshops, resolving conflicting SME definitions, and documenting the model so downstream teams queried it unaided.
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