Data Analyst interview questions
A data analyst interview should test whether the candidate can turn a vague business question into a clear analysis and explain the result to non-technical people. Check SQL and spreadsheet skills with a realistic dataset, and probe how they handle messy data and uncertain conclusions.
What to assess
Behavioral questions
Tell me about an analysis you did that changed a business decision.
What it reveals: Shows whether their work creates real impact.
A strong answer: Names the question, the data used, the finding, and the specific decision or result that followed.
Describe a time you found a significant error in a dataset or report others were relying on.
What it reveals: Reveals attention to detail and integrity.
A strong answer: Explains how they caught it, verified it, communicated it promptly, and fixed the underlying process.
Give an example of explaining a complicated finding to someone without a data background.
What it reveals: Tests communication with business stakeholders.
A strong answer: Uses plain language, a simple visual, and focuses on what the person should do with the information.
Tell me about a time a stakeholder pushed back on your numbers.
What it reveals: Shows how they defend or revise their work.
A strong answer: Revisits assumptions openly, checks the data, and either corrects the analysis or explains it with evidence.
Describe a recurring report you automated or simplified.
What it reveals: Reveals initiative and efficiency.
A strong answer: Quantifies time saved and describes tools used, such as SQL views, scheduled queries, or a dashboard.
Role-specific questions
How would you write a SQL query to find each customer's most recent order?
What it reveals: Tests core SQL skills like grouping, joins, or window functions.
A strong answer: Uses a window function or a correct subquery and mentions handling ties or nulls.
You receive a CSV with duplicate rows, missing values, and inconsistent date formats. Walk me through cleaning it.
What it reveals: Tests practical data preparation habits.
A strong answer: Profiles the data first, documents each cleaning step, and decides on missing values based on business context.
How do you choose between a bar chart, line chart, and table for a given result?
What it reveals: Reveals visualization judgment.
A strong answer: Matches chart type to the comparison being made, such as trends over time versus category comparisons.
What is the difference between correlation and causation, and how has it mattered in your work?
What it reveals: Tests statistical reasoning and caution.
A strong answer: Gives a real example and mentions confounders or the need for an experiment before claiming cause.
How would you define and calculate monthly customer churn for a subscription business?
What it reveals: Tests ability to translate business concepts into metrics.
A strong answer: Clarifies the definition, handles new and reactivated customers, and notes edge cases.
Situational questions
Sales dropped 15% last month and leadership wants to know why by tomorrow. How do you approach it?
What it reveals: Shows structured thinking under time pressure.
A strong answer: Checks data accuracy first, then segments by region, product, and channel to isolate the driver.
Two departments define 'active customer' differently and their reports conflict. What do you do?
What it reveals: Reveals how they handle data governance issues.
A strong answer: Documents both definitions, brings stakeholders together to agree on one, and updates reports consistently.
A manager asks you to present only the numbers that make their project look good. How do you respond?
What it reveals: Tests integrity with data.
A strong answer: Respectfully insists on a complete picture, offering context or framing rather than omitting results.
Motivation and fit
What kinds of business problems do you most enjoy digging into?
What it reveals: Shows motivation and domain fit.
A strong answer: Names specific problem types and connects them to the company's business.
How do you prefer to receive requests and priorities from multiple teams?
What it reveals: Reveals working style and how they manage competing demands.
A strong answer: Describes a clear intake and prioritization process and openness to adapting to the team's system.
Red flags
- Jumps to conclusions without checking data quality
- Cannot explain their own past analysis in plain language
- Treats tools as the job rather than answering business questions
- Shows willingness to cherry-pick numbers to please a manager
Questions not to ask
- How old are you? — age discrimination risk under the ADEA for candidates 40 and over
- Are you a U.S. citizen? — ask only whether they are authorized to work in the U.S. to avoid citizenship discrimination
- Do you have any health conditions that would limit long hours at a computer? — ADA restricts pre-offer disability questions
- What does your spouse do? — marital status and sex discrimination risk
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Frequently asked questions
What is a good skills test for a data analyst candidate?
Give a small, realistic dataset and one or two business questions, then ask for a short written summary or a few charts. This shows SQL or spreadsheet skill, data cleaning habits, and communication in one exercise.
What is the difference between a data analyst and a data scientist job description?
Data analysts focus on reporting, dashboards, and answering business questions with existing data. Data scientists typically build predictive models and run more advanced statistical work, so their descriptions emphasize programming and machine learning.
Should a data analyst job description list specific tools?
List the tools your team actually uses, but mark which are required versus trainable. Most analysts can switch between similar tools like Tableau and Power BI quickly, so being flexible widens your candidate pool.