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 the stack they support: log reading, SQL queries against production replicas, API calls in Postman, browser dev tools, VPN, Active Directory, or MDM tooling.
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 exact tools and commands used daily, walks through reading a stack trace or HAR file to isolate a failing call.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they decide between a workaround, a config fix, and escalation to engineering, plus how they weigh ticket volume against deep single-case investigation.
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
Explains escalation thresholds with real examples, distinguishes symptom relief from root cause, and knows when a bug report beats another reply.
03
Evaluation factor
Evidence and rigour
25% weight
Assess use of ticket data: Zendesk or Jira Service Management metrics, first response and resolution SLAs, CSAT, backlog trends, and recurring-issue tagging that triggered fixes.
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 own SLA attainment and CSAT numbers, and cites a recurring ticket pattern they documented that led to a product or KB change.
04
Evaluation factor
Collaboration and communication
15% weight
Judge written clarity with frustrated non-technical users and precision with engineers: knowledge base articles authored, reproduction steps filed, handover notes across shifts.
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 KB articles or bug tickets they wrote, adapts tone for angry customers, and reproduces issues clearly enough for engineers to act.
Put this rubric to work
Score every candidate against the same standard
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