Interview scorecard template

Graph Database Specialist interview scorecard

Evaluate Graph Database 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 probe graph modelling and traversal query depth, plus the engines they have run in production. Use the rubric to compare role-specific evidence consistently.

See AI scoring
software datadata modellinggraph databasesneo4jquery optimisation
TL;DR
For technical proficiency, look for evidence the candidate models graphs properly and writes traversals that perform, with real production experience on named engines. For systems and trade-offs, look for evidence the candidate argues for and against graph storage on the actual access patterns, and has talked someone out of a graph. 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

Probe graph modelling and traversal query depth, plus the engines they have run in production.

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

Models graphs properly and writes traversals that perform, with real production experience on named engines.

02
Evaluation factor

Systems and trade-offs

25% weight

Test when they would choose a graph over a relational or document store, and when they would not.

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

Argues for and against graph storage on the actual access patterns, and has talked someone out of a graph.

03
Evaluation factor

Evidence and rigour

25% weight

Check how they diagnose a traversal that degrades as the graph grows and supernodes appear.

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

Diagnoses supernode and traversal blowups from query plans, and can describe a model change that fixed one.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they explain a graph model to analysts and developers who think in tables.

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

Explains graph models to table-thinking colleagues so they can query independently, with usable documentation.

Evidence-led prompts

Interview questions for a Graph Database Specialist

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

  1. 01

    Walk us through how you would construct a graph model for a domain you know well. What becomes a node, what becomes a relationship, and what stays a property?

  2. 02

    How do you manage relationships in a graph database as the model evolves? Talk about direction, relationship types and cardinality.

  3. 03

    Explain traversals in a graph database and why they matter more than joins for the problems you have worked on.

  4. 04

    Can you explain path analytics and how it relates to graph databases? Where have you used it?

  5. 05

    Which graph engines have you run in production: Neo4j, Amazon Neptune, Oracle NoSQL, TigerGraph or others? What was the data volume?

See the complete Graph Database Specialist question set
Put this rubric to work

Score every candidate against the same standard

Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.

Explore AI Scorecards