Data analyst interview questions and how to answer them
Questions on the skills open data analyst listings mention and on the core work of the role. For each: what the interviewer is checking, and a STAR answer outline with placeholders for your own example.
Based on 84 open data analyst listings on ROLIVA as of . Each question below is about a term from ROLIVA’s skills vocabulary that appears in at least two of these listings, most-mentioned first.
01 Walk me through a SQL query you wrote that answered a real question. How did you know the result was right?
Why this question: SQL is mentioned in 68 of 84 listings.
What the interviewer is checking. Whether you can turn a question into joins, filters and aggregations, and whether you check your own output instead of trusting the first number the query returns.
Answer outline (STAR)
Situation.[The question someone needed answered and the tables or data sources involved, e.g. orders joined to customers].
Task.[What you had to produce, for whom, and by when].
Action.[How you built the query: the joins, filters and grouping you chose, and how you checked it, e.g. row counts, a hand-checked sample or a comparison with a known total].
Result.[What the answer showed and what was decided because of it]. If there is no number you can support, say what changed as a result or what you learned.
02 Tell me about something you built or automated in Python that other people relied on. What would you do differently now?
Why this question: Python is mentioned in 49 of 84 listings.
What the interviewer is checking. Whether you write Python that works beyond your own laptop: structure, error handling, tests or checks, and whether you can judge your earlier work honestly.
Answer outline (STAR)
Situation.[The task or problem, and who depended on the script, notebook or service].
Task.[What it had to do and any constraint, e.g. data size, run frequency or a deadline].
Action.[The approach and libraries you used, how you handled failures or bad input, and how you tested it].
Result.[What it replaced or made possible, and the one thing you would change today]. If there is no number you can support, say what changed as a result or what you learned.
03 Show me how you would explain a Tableau dashboard you built: who it was for and what it changed.
Why this question: Tableau is mentioned in 37 of 84 listings.
What the interviewer is checking. Whether you design dashboards around a question and an audience rather than around the data available, and whether you know if anyone used it.
Answer outline (STAR)
Situation.[The audience and the question they kept asking, e.g. weekly performance by region].
Task.[What the dashboard had to answer and the data source behind it].
Action.[The views, filters and calculated fields you chose, what you left out, and how you validated the numbers against the source].
Result.[How it was used, e.g. replaced a manual report or changed a weekly meeting]. If there is no number you can support, say what changed as a result or what you learned.
04 Tell me about work you did in Snowflake where performance or cost mattered. What did you change and why?
Why this question: Snowflake is mentioned in 22 of 84 listings.
What the interviewer is checking. Whether you understand how warehouse size, query design and storage choices affect speed and cost, and whether you measure before and after.
Answer outline (STAR)
Situation.[The workload, e.g. a slow transformation or an expensive scheduled query].
Task.[What you were asked to improve and the constraint, e.g. a reporting deadline or a budget].
Action.[What you investigated, e.g. query profile or warehouse usage, and the change you made].
Result.[The effect on run time or cost, measured the same way before and after]. If there is no number you can support, say what changed as a result or what you learned.
05 Describe a spreadsheet you built that people used to make a decision. How did you stop it from giving a wrong answer?
Why this question: Excel is mentioned in 19 of 84 listings.
What the interviewer is checking. Whether you can build a model or analysis others can follow, and whether you control errors: clear inputs, consistent formulas and checks against known figures.
Answer outline (STAR)
Situation.[The decision, the people making it and the data the spreadsheet held].
Task.[What the spreadsheet had to calculate or show, and how often it would be updated].
Action.[How you structured it, e.g. separate input and calculation sheets, lookups or pivot tables, and the checks you added].
Result.[The decision it informed and any error you caught before it mattered]. If there is no number you can support, say what changed as a result or what you learned.
06 Describe a time you built or changed something in Looker. How did you keep definitions consistent for everyone using it?
Why this question: Looker is mentioned in 17 of 84 listings.
What the interviewer is checking. Whether you treat shared metric definitions as a product: one agreed definition, reviewed changes, and users who can explore without breaking it.
Answer outline (STAR)
Situation.[The metric or explore in question and the confusion or need around it].
Task.[What you had to define, build or fix, and who had to agree].
Action.[How you modelled it, e.g. dimensions and measures, how changes were reviewed, and how you told users].
Result.[Whether teams stopped disputing the number, or what else changed]. If there is no number you can support, say what changed as a result or what you learned.
07 Tell me about a machine learning model you worked on that was used for a real decision. How did you know it was good enough?
Why this question: Machine learning is mentioned in 17 of 84 listings.
What the interviewer is checking. Whether you frame the problem, choose a baseline and metrics that match the decision, and check for leakage and drift rather than reporting a single score.
Answer outline (STAR)
Situation.[The decision the model supported and the data available].
Task.[Your part: framing, features, modelling, evaluation or deployment].
Action.[The baseline, the model, how you evaluated it, e.g. a held-out set or an online test, and the risks you checked].
Result.[How it was used and how it performed after release]. If there is no number you can support, say what changed as a result or what you learned.
08 Walk me through a dbt model or project you worked on. How did you test it, and what happened when an upstream source changed?
Why this question: dbt is mentioned in 13 of 84 listings.
What the interviewer is checking. Whether you use dbt as an engineering tool: modular models, tests, documentation, and a plan for upstream changes rather than silent breakage.
Answer outline (STAR)
Situation.[The data and the models involved, e.g. staging to marts for a reporting area].
Task.[What you had to build or change and who consumed the output].
Action.[How you structured the models, the tests you added, e.g. unique, not-null or relationships, and how you handled the upstream change].
Result.[What became more reliable or faster, or the incident you avoided]. If there is no number you can support, say what changed as a result or what you learned.
Questions about the core work of a data analyst
These follow from the work the role title describes, not from a count of listings.
09 Tell me about an analysis that changed a decision. What was the question, and how did you answer it?
What the interviewer is checking. Whether your work leads to action, how you frame a business question as an analysis, and how you communicate findings.
Answer outline (STAR)
Situation. Describe the business question and who asked it: [the team, the decision they faced, and the deadline].
Task. State what you had to deliver: [e.g. a recommendation, a dashboard or a one-off analysis].
Action. Explain your approach [the data sources, the method, how you checked data quality, and how you presented the findings to the audience].
Result. Give a result you can support [the decision made and its effect if measured]. If the decision did not change, say what the analysis confirmed or ruled out.
10 Describe a time you found that the data you were given was wrong or incomplete.
What the interviewer is checking. Data scepticism, the checks you run before trusting numbers, and how you raise data problems with the people who own them.
Answer outline (STAR)
Situation. Describe the data and the problem: [the source, and what was wrong, e.g. duplicates, missing values or a changed definition].
Task. Say what was at stake: [the report or decision that depended on it].
Action. Explain what you did [the check that found the problem, how you measured its impact, who you told, and how you fixed or worked around it].
Result. Share the result you can support [e.g. a corrected metric, a fix at the source]. If it could not be fixed, say how you labelled the limits of your analysis.
11 How do you handle a stakeholder who asks for a number that you think answers the wrong question?
What the interviewer is checking. Whether you can push back helpfully, clarify the underlying need and still deliver something useful on time.
Answer outline (STAR)
Situation. Describe the request: [the stakeholder, the number they asked for, and why you doubted it].
Task. State what they actually needed to decide: [the decision or goal behind the request].
Action. Explain your approach [the questions you asked, the alternative measure you proposed, and whether you delivered both].
Result. Give a result you can support [what they used and how it informed the decision]. If they preferred their original number, say how you explained its limits.
12 Walk me through a dashboard or report you built that people actually kept using. What made it useful?
What the interviewer is checking. User focus: designing for the decision rather than for the data, and maintaining trust in the numbers over time.
Answer outline (STAR)
Situation. Describe the audience and their need: [who used it and the decisions they made with it].
Task. Say what you were responsible for: [e.g. requirements, data model, build and upkeep].
Action. Explain your choices [the few measures you included and why, how you defined them, how you tested the design with users, and how you kept it accurate].
Result. Share a result you can support [e.g. regular use, a manual report it replaced]. If usage dropped, say what you learned and changed.
Using the outlines
One real example per answer. Replace every [bracketed] part with something you did and can talk about in detail.
Keep the situation short. Spend most of the answer on what you did and why.
Say “I” for your part. Name the team’s work as the team’s, and your own decisions as yours.
Results you can support. If there is no number, say what changed or what you learned. Never estimate a figure you cannot back up.
How these questions are chosen
Each open data analyst listing’s description and stated skills are checked, on whole words, against the fixed skills vocabulary ROLIVA uses on its job pages, resume examples and monthly skills reports. A listing counts once per term. A mention is not a requirement, and the counts change as employers open and close roles. The questions and outlines are preparation prompts written for this role. They are not a record of what any employer has asked, and no employer’s process is described here.
Interview Studio prepares questions and STAR outlines for a real job from its description and the experience you have confirmed. No generative AI: when a detail is missing, it asks you instead of inventing one.