Interview scorecard template

API Developer interview scorecard

Evaluate API Developer 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 REST and GraphQL design: OpenAPI/Swagger specs, versioning strategy, OAuth2 and JWT flows, pagination, idempotency keys, and gateway config in Kong, Apigee or AWS. Use the rubric to compare role-specific evidence consistently.

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software dataapi gatewaygraphqlopenapirest
TL;DR
For technical proficiency, look for evidence the candidate names concrete spec decisions, explains token refresh and idempotent retries, and shows endpoints they authored end to end. For systems and trade-offs, look for evidence the candidate weighs consumer impact against internal cleanliness, describes a deprecation timeline they ran, and explains cache and throttle choices with numbers. 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 REST and GraphQL design: OpenAPI/Swagger specs, versioning strategy, OAuth2 and JWT flows, pagination, idempotency keys, and gateway config in Kong, Apigee or AWS API Gateway.

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 concrete spec decisions, explains token refresh and idempotent retries, and shows endpoints they authored end to end.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they handled rate limiting, caching headers, backward compatible schema changes, and breaking-change deprecation windows for consumers they did not control.

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

Weighs consumer impact against internal cleanliness, describes a deprecation timeline they ran, and explains cache and throttle choices with numbers.

03
Evaluation factor

Evidence and rigour

25% weight

Test their evidence habits: contract tests with Pact, Postman or k6 load runs, p95 latency and error-rate SLOs, and how they traced a production 5xx spike.

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 latency and error budgets before and after their changes, and points to contract or load tests that caught a regression.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they support consumers: developer docs, changelogs, sandbox environments, and handling integration questions from partner or internal client teams.

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 documentation or a sandbox they built, and gives a case where partner feedback reshaped an endpoint design.

Evidence-led prompts

Interview questions for a API Developer

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

  1. 01

    Have you developed an interface from scratch, and what did that involve?

  2. 02

    Can you describe a complex interface you have worked with?

  3. 03

    Can you discuss designing an interface for a high-load system?

  4. 04

    How do you handle versioning?

  5. 05

    What is your understanding of idempotent operations in interface design?

See the complete API Developer question set
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