Interview scorecard template

Data Analyst interview scorecard

Evaluate Data Analyst 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 SQL fluency beyond basic joins: window functions, CTEs, query tuning on large tables, plus dbt, Python (pandas), and BI tools like Looker, Tableau or Power. Use the rubric to compare role-specific evidence consistently.

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software dataab testingdashboardsdata modellingsql
TL;DR
For technical proficiency, look for evidence the candidate writes complex SQL from memory, names warehouse specifics (Snowflake, BigQuery), and describes dbt models or dashboards they built and maintained. For systems and trade-offs, look for evidence the candidate explains schema and metric definition choices with trade-offs, and cites a case where a shortcut later caused rework or reconciliation pain. 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 SQL fluency beyond basic joins: window functions, CTEs, query tuning on large tables, plus dbt, Python (pandas), and BI tools like Looker, Tableau or Power BI.

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

Writes complex SQL from memory, names warehouse specifics (Snowflake, BigQuery), and describes dbt models or dashboards they built and maintained.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they modelled messy source data: handling duplicate events, slowly changing dimensions, late-arriving records, and choosing between a one-off query and a governed metric definition.

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 schema and metric definition choices with trade-offs, and cites a case where a shortcut later caused rework or reconciliation pain.

03
Evaluation factor

Evidence and rigour

25% weight

Test analytical rigour: sample sizing, significance testing on an A/B experiment, confounders, and how they validated numbers before a metric reached an executive dashboard.

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

Distinguishes correlation from causation with a real example, quantifies uncertainty, and describes QA checks that caught an error before publication.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they turn analysis into decisions: stakeholder scoping of ambiguous requests, presenting findings to non-technical leaders, and handling pushback when data contradicts a favoured plan.

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

Names a decision that changed because of their analysis, and describes reframing a vague request into a measurable question.

Evidence-led prompts

Interview questions for a Data Analyst

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 a time when you had to use data to make a decision?

  2. 02

    Can you describe a project where you used large datasets?

  3. 03

    Can you describe your experience with predictive modelling?

  4. 04

    What is your proficiency level in SQL?

  5. 05

    What is your experience with Python or R for data analysis?

See the complete Data Analyst question set
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