Interview scorecard by role
Data Analyst Interview Scorecard & Questions
Data analysts turn messy data into decisions. The core skills are SQL, careful reasoning about data quality, and the ability to explain findings to non-technical colleagues. A short take-home or live SQL exercise beats any amount of talking.
What to assess: 5 core competencies
Copy this scorecard. Assign each competency to one interviewer, rate it 1–5 and write down the evidence before discussing the candidate with anyone.
| Competency | What good looks like | Score | Evidence |
|---|---|---|---|
| SQL and data handling | queries, joins and cleans data correctly. | ||
| Analytical reasoning | frames questions and avoids misleading conclusions. | ||
| Visualization | builds clear charts and dashboards people actually use. | ||
| Communication | explains findings and limitations to non-experts. | ||
| Business sense | focuses on analyses that change decisions. | ||
| Overall | ☐ Strong no ☐ No ☐ Yes ☐ Strong yes | ||
10 structured interview questions for a data analyst
Ask the same core questions, in the same order, to every candidate. Probe with "What did you do?" and "What was the result?"
- Tell me about an analysis that changed a business decision.
- Describe a time the data was wrong. How did you discover it?
- How would you calculate monthly retention from an orders table?
- Explain the difference between correlation and causation using an example from your work.
- Tell me about a dashboard you built. Who used it, and how did you know?
- How do you handle a stakeholder who wants data to prove a conclusion they already have?
- What's your process for validating a new data source?
- Describe explaining a statistical result to a non-technical manager.
- How do you prioritize many ad-hoc requests?
- Exercise: write SQL for top 5 customers by revenue in the last 90 days, then explain edge cases.
Rating anchors for the critical competencies
Anchors stop one interviewer's "4" from being another's "2". These cover the two competencies that most often separate strong and weak data analyst hires. Read our interview rating scale guide to write anchors for the rest.
SQL and data handling
| 1 — Below the bar | Struggles with joins; ignores nulls and duplicates. |
|---|---|
| 3 — Meets the bar | Writes correct queries and checks row counts. |
| 5 — Exceptional | Writes clean, efficient SQL, anticipates edge cases and documents assumptions. |
Communication
| 1 — Below the bar | Presents tables of numbers without a conclusion. |
|---|---|
| 3 — Meets the bar | Leads with the insight and supports it with a clear chart. |
| 5 — Exceptional | Tailors the story to the audience, states uncertainty and recommends an action. |
Red flags to watch for
- Never questions data quality.
- Overstates certainty.
- Can't explain what their past analyses led to.
30-60-90 day plan outline for a new data analyst
| Days 1–30: Learn | Get data access, learn the schema and key metrics, rebuild one existing report to validate understanding. |
|---|---|
| Days 31–60: Contribute | Deliver two analyses for stakeholders; fix one data quality issue. |
| Days 61–90: Own | Own a core dashboard and propose an analysis roadmap. |
Turn this into a full plan with goals and check-ins using our 30-60-90 day plan guide or the 30-60-90 Day Onboarding Kit.
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How many interview rounds should a data analyst go through?
Two or three focused stages are usually enough for a small company: a short screen, a structured interview using the questions above, and a practical exercise or final interview. Score every stage on the same 1–5 scale.
What is the most predictive part of a data analyst interview?
The practical exercise — "write SQL for top 5 customers by revenue in the last 90 days, then explain edge cases." — because it shows real work rather than descriptions of it. Score it against the anchors on this page.
Generate more questions with the free interview question generator.