Interview Questions for a Data Analyst
To interview a data analyst, test SQL fluency, metric definition, dashboarding, and the ability to turn data into a clear business recommendation. This set covers joins and aggregation, data cleaning, choosing the right chart, A/B reasoning at a practical level, and how candidates spot data-quality issues before they mislead a decision.
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Run a data analyst interview with a live SQL exercise and a business case where the candidate must define a metric and recommend an action. Reward clarity, skepticism about data quality, and communication over fancy techniques.
Technical & Role-Specific
What to look for: Correct use of window functions or a subquery, handles ties, and reasons about NULLs and empty departments.
What to look for: Understands how a LEFT JOIN preserves unmatched rows and how an INNER JOIN silently drops data, a common source of wrong metrics.
What to look for: Recognizes that ambiguous definitions break comparisons, sets explicit time windows and criteria, and documents them.
What to look for: Systematic profiling, deduping on a key, standardizing formats, deciding how to treat nulls, and validating row counts before and after.
What to look for: Understands skew and outliers, uses median for skewed distributions like income or latency, and reports the right summary.
What to look for: Matches chart to intent: trends over time, category comparison, or precise values, and avoids misleading visuals like truncated axes.
What to look for: Traces the number to source, checks the pipeline and filters, reconciles against a known total, and rules out data quality before alarm.
What to look for: Correctly anchors cohorts by signup period, uses date logic and self-joins or window functions, and avoids double-counting users.
Behavioral
What to look for: Connects analysis to outcome, communicated clearly to decision-makers, and understood the business context not just the data.
What to look for: Healthy skepticism, attention to detail, and the habit of sanity-checking results before publishing them.
What to look for: Transparency about limitations, fixing or flagging the data issue, and not delivering a confident-looking but wrong report.
What to look for: Empathy for the audience, ruthless focus on the decision the report drives, and removing noise rather than adding charts.
Situational / Problem-Solving
What to look for: Clarifies the decision behind the request, scopes the question, and delivers a focused answer rather than a data dump.
What to look for: Compares definitions, filters, time zones, and refresh times, and finds the source of divergence systematically.
What to look for: Prioritizes a defensible directional answer, states assumptions and caveats, and avoids false precision under time pressure.
What to look for: Checks for instrumentation changes, deploys, seasonality, and bot traffic before declaring it a genuine business shift.
Data Analyst interview scorecard
Score every candidate on the same criteria, immediately after the interview, using evidence you actually heard rather than an overall impression. Agree the criteria with the panel before the first interview — deciding what counts after you have met people is how the loudest interviewer wins the debrief.
| Criterion | Evidence to record | Score 1-5 |
|---|---|---|
| Technical & Role-Specific | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Behavioral | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Situational / Problem-Solving | What the candidate actually said or did, in their own example — not your impression of it | 1 2 3 4 5 |
| Overall recommendation | Strong no / no / mixed / yes / strong yes, with the single reason that decided it | - |
Want this as a reusable document? Use the interview scorecard template.
Questions to avoid asking a Data Analyst
Exactly which questions are unlawful depends on where you are hiring, and the rules change — so treat this as the list of topics to route through your own employment counsel, not as a legal standard. The practical test that holds everywhere: if the answer could not change how the person does this job, you have no reason to ask it.
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Frequently asked questions
How many interview rounds for a Data Analyst?
How important is SQL for a data analyst?
What is the difference between a data analyst and a data scientist interview?
Should I test visualization and communication skills?
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