Interview scorecard template

Business Intelligence Developer interview scorecard

Evaluate Business Intelligence Developer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so test SQL depth beyond joins: window functions, CTEs, query plans, incremental models in dbt, plus DAX or MDX measures and semantic layer design in Power. Use the rubric to compare role-specific evidence consistently.

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software datadbtdimensional modellingpower bisql
TL;DR
For technical proficiency, look for evidence the candidate writes tuned SQL against warehouses like Snowflake or BigQuery, explains star schema grain choices, and debugs DAX filter context fluently. For systems and trade-offs, look for evidence the candidate names a concrete trade-off, for example moving from direct query to imported aggregates, with the refresh and cost consequences they accepted. 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

Test SQL depth beyond joins: window functions, CTEs, query plans, incremental models in dbt, plus DAX or MDX measures and semantic layer design in Power BI, Tableau or Looker.

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 tuned SQL against warehouses like Snowflake or BigQuery, explains star schema grain choices, and debugs DAX filter context fluently.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they chose between wide denormalised tables, aggregate extracts and live connections, and what they did about slow dashboards, row-level security and cost per query.

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 a concrete trade-off, for example moving from direct query to imported aggregates, with the refresh and cost consequences they accepted.

03
Evaluation factor

Evidence and rigour

25% weight

Assess how they validate numbers: reconciliation against source systems, unit tests on models, data quality checks, and what happened when finance disputed a reported figure.

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

Describes reconciliation routines and tests that caught discrepancies before stakeholders did, with named checks and the defect they prevented.

04
Evaluation factor

Collaboration and communication

15% weight

Look for evidence of requirements gathering with non-technical users: metric definition workshops, dashboard adoption rates, documentation in a data catalogue, and training or handover to analysts.

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 dashboards people actually use, agreed metric definitions written down, and a habit of pushing back on vague reporting requests.

Evidence-led prompts

Interview questions for a Business Intelligence Developer

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

  1. 01

    Can you give an example of using data to influence a business decision?

  2. 02

    Have you been involved in the full lifecycle of a reporting project?

  3. 03

    Do you have experience creating dashboards, and can you give examples?

  4. 04

    How comfortable are you with querying data directly for analysis?

  5. 05

    Do you have experience working with data warehouses?

See the complete Business Intelligence Developer question set
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