Interview scorecard template

Distributed Systems Engineer interview scorecard

Evaluate Distributed Systems 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 probe depth on consensus and replication: Raft or Paxos internals, leader election, quorum reads, exactly-once semantics, and hands-on work with Kafka, etcd, Cassandra or Spanner. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical proficiency, look for evidence the candidate explains quorum trade-offs, clock skew and partition behaviour precisely, citing config details from systems they personally operated at scale. For systems and trade-offs, look for evidence the candidate names the specific trade-off made (for example CP over AP), the failure it prevented, and the cost it introduced. 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 depth on consensus and replication: Raft or Paxos internals, leader election, quorum reads, exactly-once semantics, and hands-on work with Kafka, etcd, Cassandra or Spanner.

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

Explains quorum trade-offs, clock skew and partition behaviour precisely, citing config details from systems they personally operated at scale.

02
Evaluation factor

Systems and trade-offs

25% weight

Ask how they sized shards, chose consistency levels, handled backpressure and hot partitions, and what they gave up on latency, cost or availability to get there.

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

Names the specific trade-off made (for example CP over AP), the failure it prevented, and the cost it introduced.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they prove a distributed system correct: Jepsen-style fault injection, chaos experiments, load tests, p99 tail latency budgets, and postmortems for cascading failures.

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 measured before and after numbers, reproduced a race with fault injection, and traced root cause through logs, metrics and traces.

04
Evaluation factor

Collaboration and communication

15% weight

Look for evidence of design docs, RFCs, on-call runbooks and incident reviews shared with SREs and product teams during multi-region migrations or protocol upgrades.

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

Points to a circulated design doc or postmortem that changed a team decision, and explains dissent handled without drama.

Evidence-led prompts

Interview questions for a Distributed Systems Engineer

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

  1. 01

    Describe the most complex distributed system you have worked with.

  2. 02

    Provide an example of how you used distributed systems to solve a business problem.

  3. 03

    Can you describe a time when you solved a problem in a distributed infrastructure?

  4. 04

    How would you handle a failure in a distributed system?

  5. 05

    What strategies do you use for data replication and sharding?

See the complete Distributed Systems Engineer question set
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