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 depth on both ends: React or Vue state handling, Node or Django API layers, SQL schema design, migrations, and how they handle auth flows like OAuth or JWT.
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 frameworks and versions precisely, explains query optimisation or component re-render fixes, and shows comfort from database index to browser paint.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe architecture calls they owned: REST versus GraphQL, monolith versus service split, caching with Redis, background jobs, and what they knowingly deferred as technical debt.
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
Walks through a specific trade-off with load numbers, cost, and team size, plus the failure mode they accepted and later revisited.
03
Evaluation factor
Evidence and rigour
25% weight
Test how they prove code works: test pyramid coverage, Playwright or Jest suites, CI gates, error budgets, and how they debugged a production incident from logs or traces.
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 concrete evidence such as p95 latency before and after, Sentry error rates, or a failing test that caught a regression pre-release.
04
Evaluation factor
Collaboration and communication
15% weight
Assess how they work with designers, product managers, and reviewers: pull request review habits, ticket scoping, handling Figma handoffs, and unblocking others during sprints.
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 review comments that changed a design, negotiates scope with product owners, and writes docs or runbooks colleagues actually use.
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