Interview scorecard template

DevOps Engineer (Remote) interview scorecard

Evaluate DevOps Engineer (Remote) 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 hands-on command of Terraform or Pulumi, Kubernetes primitives, and CI runners (GitHub Actions, GitLab, ArgoCD); ask what they wrote versus inherited from a platform. Use the rubric to compare role-specific evidence consistently.

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software dataci cdkubernetesobservabilityterraform
TL;DR
For technical proficiency, look for evidence the candidate names modules, Helm charts and pipeline stages they authored, explains state locking, drift and rollout strategies without hedging. For systems and trade-offs, look for evidence the candidate compares options with cost, blast radius and on-call burden attached, and cites a trade-off they later reversed with reasons. 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 hands-on command of Terraform or Pulumi, Kubernetes primitives, and CI runners (GitHub Actions, GitLab, ArgoCD); ask what they wrote versus inherited from a platform team.

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 modules, Helm charts and pipeline stages they authored, explains state locking, drift and rollout strategies without hedging.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe architecture calls: blue/green versus canary, managed RDS versus self-hosted, multi-AZ cost versus availability, and where they deliberately accepted toil or lock-in.

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

Compares options with cost, blast radius and on-call burden attached, and cites a trade-off they later reversed with reasons.

03
Evaluation factor

Evidence and rigour

25% weight

Test measurement habits: SLOs and error budgets, p99 latency, DORA metrics, Prometheus alert tuning, and a postmortem they wrote after a production incident.

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

Quotes before and after numbers (deploy frequency, MTTR, cloud spend) and describes alert noise reduction backed by real dashboards.

04
Evaluation factor

Collaboration and communication

15% weight

Assess remote working evidence: async handovers across time zones, runbooks developers actually use, on-call rotation etiquette, and pushing back on unsafe release requests.

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

Shows written runbooks or RFCs, describes coaching developers to self-serve deploys, and handles disagreement with product without escalation drama.

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