Interview scorecard template

Site Reliability Engineer interview scorecard

Evaluate Site Reliability Engineer 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 depth in Linux internals, Kubernetes, Terraform, and observability stacks: ask how they set SLOs, wired Prometheus alerts, or cut noisy pages using error budgets. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical proficiency, look for evidence the candidate names specific tooling and versions, explains SLI selection and burn-rate alerting, and shows real command of container and network internals. For systems and trade-offs, look for evidence the candidate reasons about blast radius, quorum, and cost per nine; defends choices with concrete load numbers rather than best-practice slogans. 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 depth in Linux internals, Kubernetes, Terraform, and observability stacks: ask how they set SLOs, wired Prometheus alerts, or cut noisy pages using error budgets.

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 tooling and versions, explains SLI selection and burn-rate alerting, and shows real command of container and network internals.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe design trade-offs on multi-region failover, autoscaling limits, and cost: ask where they accepted lower availability deliberately and what capacity headroom they ran.

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

Reasons about blast radius, quorum, and cost per nine; defends choices with concrete load numbers rather than best-practice slogans.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they measure reliability: p99 latency, MTTR, change failure rate, postmortem actions closed. Ask for a specific incident timeline and the root cause they proved.

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 before-and-after metrics, describes blameless postmortems with tracked action items, and distinguishes correlation from verified root cause.

04
Evaluation factor

Collaboration and communication

15% weight

Assess on-call life: how they ran handovers, wrote runbooks, pushed toil back to product teams, and coordinated an incident as commander across dev and support.

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 clear incident-command roles, runbooks others actually used, and negotiated reliability work into product roadmaps without friction.

Evidence-led prompts

Interview questions for a Site Reliability Engineer

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

  1. 01

    Can you describe your experience configuring and managing Linux systems?

  2. 02

    Do you have experience with container technology such as Docker or Kubernetes?

  3. 03

    How have you contributed to infrastructure as code, and what did you own?

  4. 04

    Do you have experience automating routine tasks? Can you give a specific example?

  5. 05

    Can you describe the most challenging system issue you have encountered and how you resolved it?

See the complete Site Reliability Engineer question set
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