Data analyst behavioral interview questions are tricky to answer.
They seem benign and easy. However, each question is an opportunity to show your value and make it clear you’re the best candidate for the position - an opportunity competing candidates will have as well, so you want to make the most of it to land the job.
Generally, these behavioral questions for data analysts are open-ended and are designed to assess your experience, your ability to communicate, and whether you’re the right fit for the company and role.
You can expect behavioral questions in these five areas:
Specificity is the key to answering data analyst interview questions. Use specific examples, clearly outline steps you took to solve problems and describe any lessons or new skills you learned in the past. One helpful tip: Consider using a framework to structure your answers.
The STAR method is a commonly used framework for behavioral questions. With this method, you would first describe the Situation, cover the Task you had to solve, describe the Action you took, and finally, highlight specific Results you achieved.
This is particularly helpful for outlining past projects and handling experiential questions. For example:
You might respond with:
This is a general outline. You’ll want to add more depth, but it gives you an idea of how to use the STAR framework to structure your answers.
Asking data analysts behavioral interview questions is useful for cross-checking a resume and seeing if someone’s career level aligns with the role. Therefore, a lot of behavioral questions for data analysts will explore prior experiences, past projects, and how you’ve handled adversity in the past.
When you’re asked about a project, use a format like the STAR method. You should walk the interviewer through the project, from start to finish. Begin with the business problem and conception. Describe your approach and how you executed it. And always end with the results.
Hint: Project questions give you a chance to show off your iterative process and how well you work with stakeholders.
Data analysts get tasked with experimenting with data to test new features or campaigns. Many behavioral questions will ask about experiments but also tap into how you approach measuring your results.
With questions like these, be sure to describe the objective of the experiment, even if it is a simple A/B test. Don’t be afraid to get technical; explain the metrics you used and the process you used to quantify the results.
With a question like this, remember these tips:
You might say:
“I was in charge of creating an important data analytics report in my previous role. Due to an ETL error, the data we were using for the project the data wasn’t available. As the deadline approached, I knew the report wouldn’t be finished, so I informed my manager about the issue, provided a revised timeline for when it would be done and worked with the data engineering team to fix the ETL error.”
A good clarifying question would be: “What do you consider a large dataset?” This won’t necessarily change your answer, but it will show that you’re detail-oriented.
Note: If you haven’t worked with a “large” dataset, choose a project with a smaller dataset that required a lot of data cleaning and describe how you might scale what you learned to a larger dataset.
Many behavioral questions will assess your ability to communicate tools and techniques, your results, and insights to a lay audience.
You’ll find a lot of variations to this question, but the objective is always the same: to assess your ability to communicate complex subject matter and make it accessible. Data analysts often work cross-functionally, and this is a key skill they must possess.
Have a few examples ready and use a framework to describe them. You might say:
“The marketing team wanted to better segment customers, so, after gaining an understanding of their motivations and goals for the project, I presented several segmenting options and talked them through trade-offs.
I felt that K-means clustering would be the best method for their objective, so I made a presentation about how the method worked, potential strategies for visualizing the new segments, described key benefits, and ultimately, talked about potential trade-offs.”
Interviewers want to know you’re confident in your communication skills and can effectively communicate complex ideas. With a question like this, walk the interviewer through your process:
Also, the ability to present virtually is vitally important in today’s market. Have several recent experiences to talk about, both in-person and virtual. This is a common question in data visualization interviews.
Interviewers ask a question like this to see if you can make insights actionable. You might say:
“In my previous position, I was in charge of identifying opportunities to optimize our marketing efforts. Specifically, I was analyzing the types of ads that were generating conversions. Through my analysis, it became clear that one type of ad worked on Platform A, but not Platform B. I was able to persuade the marketing team to optimize the ads it created for Platform B, resulting in a 10% lift in conversions.”
A question like this is designed to learn about your data visualization and reporting knowledge. Plus, it assesses if you’re comfortable and know how to present data insights. Data presentation techniques you might talk including:
A question like this explores how you handle adversity and adapt in the moment. Be honest about what went wrong. Then, describe how you apply what you learned to future tasks.
For example, you might say:
“I presented a data analytics project to non-technical stakeholders, but my presentation was far too technical. I realized that the audience wasn’t following the technical aspects, so I stopped and asked for questions. I spent time clarifying the technical details until there were no questions left. One thing I learned was that it’s important to tailor presentations to the audience, so before I start a presentation, I always consider the audience.”
Interviewers want to see that you’re data savvy, and that you can assess data quickly, know when something’s amiss, or have ideas about where to start when investigating a problem.
Successful data analysts help businesses identify anomalies and respond quickly. You might say:
“I was working with a univariate dataset, which would follow a fairly normal distribution. Before jumping into analysis, I ran a normality test and the distribution looked skewed.”
When working on an analysis, you’ll likely have a prediction about what you’ll find. How do you respond when your prediction is wrong? This question gets asked to see a) if you’re open to change, and b) that you’re dedicated to making data-driven decisions.
Your answer might be:
“While working on a customer analytics project, I was surprised to find that a subsegment of our customer base wasn’t actually responding to the offers we were providing. We had lumped the subsegment into a larger customer bucket and had assumed that a broader segmentation wouldn’t make a difference. I relayed the insight to the marketing team, and we were able to reduce churn among this subsegment.”
This is the data analyst version of the classic behavioral question: “Tell about a time you made a mistake at work.” Remember, you don’t want to blame someone else in your response. Be honest about the error, describe what you did, and tell the interviewer what you learned.
You might say:
“Due to a mislabeling error, I was using the wrong data for a conversion analysis project. The data I was using wasn’t current. I was able to spot the error after checking for minimum and maximum conversion rate values, and noticing that the range seemed off.”
This is an open-ended question that interviewers use to a) understand your experience, b) assess your decision-making skills, and c) understand how you take action based on insights. In your answer:
When asked about dealing with insufficient data during interviews, structure is key. Here’s a quick guide:
This structured response ensures clarity and succinctness, highlighting your analytical journey even when faced with data challenges.
These questions are designed to see if you’re the right match for the team. They assess your passion for analytics, how you work with others, and why you want to work for the company.
This question gets asked a lot, especially for entry-level data analyst positions, and yet, it trips up a lot of candidates. It’s not enough to simply state, “I have always loved statistics.” Be honest about what makes you passionate about data and analytics.
Maybe it was a project you did in an undergraduate class or a book you read that ignited your curiosity. Maybe you read an interesting case study and wanted to help businesses better utilize data. The key is to show a genuine passion for data and analysis in your response.
Again, this is a super common question that trips up a lot of candidates. Have a strong answer to this question. You might focus on the company’s data culture, or you might mention a connection you have to the company (e.g., a former colleague).
For a Meta data analyst interview you might say:
“I’m excited by the possibility of using data to foster stronger social connections amongst friends and peers. I also like to ‘go fast’ and experiment, which fits into Meta’s innovative culture.”
With this question, the interviewer is probing your work style and your passion for analytics. In your response, you might include these qualities of a data analyst:
Your response should be tailored to the position and where you are in your career. For example, an entry-level data analyst might say:
“I love analytics in general, and I’ve always excelled in statistics. I have a strong interest in investigating problems with data. However, at this point in my career, I know I have a lot to learn, want to gain experience, and work on a lot of different tasks and projects.”
Someone with more experience might tailor their response to the niche they’ve worked on or are most passionate about.
This is a classic culture fit behavioral question. Interviewers ask it to see how well you take direction, how you collaborate, and how you might fit in with the team. Your response might be:
“My last job was at a start-up, and I essentially had to build the analytics processes from the ground up. As a start-up, we had to move quickly, which was a great experience because I learned continuous iteration techniques to maintain high output with seemingly impossible deadlines. In that job, I also had to work closely and collaborate with various teams and help build analytics solutions tailored to various stakeholder needs. I really enjoyed serving others and building reporting solutions that made their lives easier.”
Handling feedback effectively showcases one’s adaptability and commitment to growth.
A good response to the following question would be:
“Throughout my career, I’ve come to view feedback as a tool for refining skills and enhancing team cohesion. I approach it with an open mind, focusing on the substance of what’s being shared. Over the years, I’ve also made it a point to seek feedback proactively, as it helps in staying aligned with team and organizational objectives”
This type of question is used to probe your work experience and understand the types of analytics problems and projects you’ve worked on. Scenario questions often start with “What would you do if…” and ask you to describe your approach.
This question assesses how you deal with ambiguity, set priorities, and your decision-making process in unclear situations. You could say:
“I joined a company that had just started embracing data analytics. My role hadn’t really been defined and day-to-day responsibilities were a bit of a clean slate. In my first 30 days, I spent a lot of time organizing the existing analytics tools, as well as learning the company’s core objectives. Then, I created a plan for aligning analytics output for the next 6 months to those core objectives, which I presented to my manager. Together we prioritize tasks, and we were able to quickly scale up the company’s analytics capabilities.”
This is more of a technical question, and you might be provided with a sample dataset to help solve it. If your response was more generic, you might describe how the drop could be a result of declining sales, of rising costs, or a mix of both. You might say:
“If I noticed a drop in revenue, I’d first check to identify if it was, in fact, out-of-line with historical revenue data. Then, I’d gather metrics like revenue per marketing share, profit margin per item sold, and revenue by project and category, and discounts applied. This would help us begin to explore potential causes for the drop.”
This question is a bit of an ambiguous time management question because you have day-to-day responsibilities to consider. You might say:
“Before approaching leadership, I would set aside a few hours to do the initial research, while maintaining my day-to-day tasks. If, after that initial research, I did believe that the project would have an impact, I’d communicate with my boss about the project, provide an overview of the opportunity costs, and work with my manager to gain feedback and direction on how to proceed.”
For a question like this, always start with clarifying questions. You might ask:
“Have there been any internal changes recently? Is it a particular type of ride? What is the timeframe?”
Then, talk through the external and internal factors you would consider in your investigation. External factors might include:
On the internal side, you might talk about metrics like cancellations by device type, a change in cost per ride or average time per pick-up, or UI changes.
Interview Query offers a variety of resources to help you ace your interview: