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Data Analyst Interview Questions

Edited by Indeed Employer Content Team
Last updated April 8, 2025
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Indeed’s Employer Resource Library helps businesses grow and manage their workforce. With over 15,000 articles in 6 languages, we offer tactical advice, how-tos and best practices to help businesses hire and retain great employees.

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Data analysts collect and interpret data to identify trends and patterns. Data analyst duties and responsibilities typically include extracting information from large data sets, performing statistical analysis to make predictions and identifying and recommending new ways to improve a business based on data.

When hiring a data analyst, look for ​​strong problem-solving skills, knowledge of data analysis tools and languages (e.g., SQL, Excel) and a good understanding of the data analysis process. Great candidates for your data analyst role may also have big picture thinking skills, an investigative mindset and proficiency with one or more scripting languages (Python, R, etc.).

Ask 5-10 of the following interview questions to get a better sense of a candidate’s data analysis skills and experience.

Whether you are preparing to interview a candidate or applying for a job, review our list of top Data Analyst interview questions and answers.

  1. What do data analysts do? See answer
  2. Which data analysis software are you well-versed in? See answer
  3. Explain how you would estimate potential shoe sales in New York City each June. See answer
  4. Why did you go into data analysis? See answer
  5. Describe a situation where data cleaning factors into your data analysis tasks. See answer
  6. How do you ensure the reliability and accuracy of your data analysis? See answer
  7. How would you manage analyzing a large dataset under a tight deadline? See answer
  8. Please talk about a time when you couldn’t meet a deadline.
  9. What was your most difficult data analysis project?
  10. What’s your process when starting a new project?
  11. How do you stay up to date with the latest data analysis trends and technologies?
  12. How do you approach datasets with missing or inconsistent information?
  13. How would you validate data analysis results to nontechnical stakeholders?
  14. How do you determine which metrics or KPIs to focus on for new projects?
  15. How would you define the Hierarchical Clustering Algorithm? When would you use it?
  16. Can you tell me how to use logistical regression when analyzing data sets?
  17. What is the difference between big data and data? Do you have experience working with big data?
  18. How many statistical methods are you familiar with? Can you provide me with a few examples?
  19. Are you comfortable using data analysis software? Which programs have you used in previous roles?
  20. Can you give me an example of when you would use data profiling or data mining?
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Hire your next Data Analyst today.

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Hire your next Data Analyst today.

Post a job
Our mission

Indeed’s Employer Resource Library helps businesses grow and manage their workforce. With over 15,000 articles in 6 languages, we offer tactical advice, how-tos and best practices to help businesses hire and retain great employees.

Read our editorial guidelines
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10 Data Analyst Interview Questions and Answers

Close-up shot of a data analyst. Graphs, numbers, and charts can be seen in the reflection of their glasses.Text reads:

What do data analysts do?

This question is basic, but it serves the essential function of identifying the candidates who have a basic understanding of data analysis. It also lets you compare how well various candidates understand the job. What to look for in an answer:

  • In-depth coverage of each step
  • Mention of soft skills, such as communication
  • Discussion of how data analysts benefit companies
Example:

"In general, data analysts collect, clean and crunch data for insight that helps their companies make better decisions. They look for correlations and communicate their results well to diverse audiences. Data analysts must also demonstrate critical thinking and creativity by spotting opportunities for improvement and for implementing preventative measures."

Please talk about a time when you couldn't meet a deadline.

This question gets into how well candidates handle stressful situations. You want a 1045468422data analyst68.12analyst7.26business analyst7.21data5.78junior data analyst2.4596518685090922000.0084680.86187000.00YEARLY20291033092025-09-012025-09-302025-08-012025-08-312025-10-05data analyst who can anticipate when a deadline isn't going to work and find a solution that works for stakeholders. Past behavior offers a good predictor of future behavior. What to look for in an answer:

  • The ability to see the big picture
  • Decisiveness and a proactive approach
  • Answers that don't blame others
Example:

"At my former company, my team had a hard time finding relevant data to do an environmental impact study. I contacted the client, explaining our challenges and our attempts to remedy the problem. Because it was still relatively early in the process, I secured a one-week extension to find a workable solution."

Which data analysis software are you well-versed in?

This question lets you assess if candidates have the hard skills you need or if you may need to train them on your company's preferred apps and tools. It's also another way to ensure basic competency in data analysis. What to look for in an answer:

  • Proficiency in software your job ad emphasized
  • Willingness to learn new tools for the job
  • Ability to discuss software with familiarity
Example:

"I have a breadth of software and programming experience. For example, at my current employer, I regularly use Power BI for data visualization, Tableau for business intelligence and SAS for creating reports with robust insights. I also program in Python using interfaces such as Snowflake when I need to make my own tools."

What was your most difficult data analyst project?

With a question like this, you can gain insight into how candidates approach and solve problems. It also gives you a better idea of the type of work they've done. What to look for in an answer:

  • Explanation of how challenge(s) were overcome
  • Ownership and accountability of their process
  • Discussion of why the project was difficult
Example:

"My most difficult project was predicting how many endangered animals would survive to 2020, 2050 and 2100. Before this, I’d dealt with existing data and past events. So, I researched the various habitats, the animal’s predators and other factors and based my predictions on that information. I have high confidence in the results."

Explain how you would estimate potential shoe sales in New York City each June.

Many interviewers pose questions that let them see a data analyst’s thought process without the aid of computers and data sets. After all, technology is only as good and reliable as the people behind it. What to look for in an answer:

  • The ability to identify variables/data segments
  • The capability to communicate their thought process
  • Creativity with their responses
Example:

"First, I'd gather population data for New York City and determine the number of tourists who visit in June and their average length of stay. From there, I’d break down the numbers by age, gender and income and look for data that shows how many shoes they may already have. I’d also figure out their motivations to buy new shoes."

What's your process when starting a new project?

This question lets you measure candidates’ organizational skills and how well they anticipate steps to complete projects. It also gives you an opportunity to see if candidates have compatible leadership or work styles for your company culture. What to look for in an answer:

  • Clear steps
  • Deliberate processes
  • Consideration of deadlines
Example:

"My first step is taking time to look over the project so I can define the objective or problem. If I’m having a hard time figuring that part out, I reach out to the client. Next, I feel out the data to see what’s there and its reliability and sources. I think about possible ways to model the data and whether the project deadline seems to work.”

Why did you go into data analysis?

This query helps you get to know candidates as people. It works equally well as an icebreaker at the beginning of an interview or, if it comes at the end, as a gentle way to bring your questions to an end. What to look for in an answer:

  • Focused reasoning
  • A feel for their personality
  • Passion and dedication
Example:

"When I was 10, I wanted a paper route to earn money for a class trip, but my dad said no. I took it upon myself to give him a report on how much I would earn, how long it would take and why trade-offs like lack of sleep were worth it. In the process, I fell in love with data analysis."

Describe a situation where data cleaning factors into your data analysis tasks.

This question assists interviewers in gauging a candidate's ability to isolate clean data to use in their analysis. It also helps them determine a candidate's level of experience with data cleaning in their daily job. The candidate's answer should emphasize:

  • Data cleaning competency
  • Strong attention to detail
  • An investigative mindset
Example:

"When pulling sales quota data from the past three months, I know that all data doesn't get entered the same. Some employees may enter their numbers as percentages, while others might enter them as monetary figures. This means I need to identify these inconsistencies as I retrieve data and standardize the results to reflect one entry method or the other. This consistency lets me complete my analysis with accurate statistical calculations."

How do you ensure the reliability and accuracy of your data analysis?

This question homes in on your candidate’s attention to detail when dealing with their work product. It also reveals the methods they use to maintain data integrity. Here’s what to look for in their answer:

  • An understanding of data validation and verification
  • Techniques to prevent and correct errors
  • A focus on reliable, consistent outcomes
Example:

"I ensure data reliability and accuracy by cross-verifying data sources to validate information. I employ statistical methods to identify and manage outliers, which helps me maintain the integrity of my data sets. I may also automate data cleaning processes to streamline this phase and reduce the margin of error in my outputs."

How would you manage analyzing a large dataset under a tight deadline?

The question assesses your candidate’s technical prowess and highlights soft skills. It can help interviewers learn a lot about how the candidate manages time, stress and communication, and answers reflect how they prioritize their work. A compelling answer demonstrates:

  • Strategic technical thinking
  • Essential soft skills
  • Project management capabilities
Example:

"During a project with a tight deadline and massive dataset, I broke everything down into smaller pieces, enabling me to focus on high-priority tasks first. I used analytical tools to clean and process the data first, and I kept an open line of communication with stakeholders to clarify information and provide progress updates. As a result, we met the deadline with high-quality output."

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