30 Interview Questions to Prepare for Your Next Data Analyst Role

Sarah Rajani
3 days ago
19 min read

Updated: 46 minutes ago

A practical guide to data analyst interview questions, including what they’re really asking, and how to answer with confidence using your own experience

If you're preparing for data analyst interviews, you're probably getting hit with the usual advice: learn SQL, review business questions, practice behavioral stories.

When I started prepping for my first data analyst interview, I wasn’t only worried about SQL or dashboard questions. I was confused about the open-ended questions I might get. Questions I've heard that get asked, like “Tell me about a time you handled ambiguity.” Even basic ones like “Why do you want this role?” felt hard to answer. I didn't know how I would answer these or even what to the interviewer might be looking for.

The problem wasn’t that I didn’t know what I was doing. It was that I wasn’t sure how to talk about what I’d done, or how to prepare. I hadn't had an interview in a long time, and certainly not a data analyst interview.

So I pulled together 30 of the most common questions I’ve come across during my data analyst interviews. It’s not just a list of questions you might be asked, but it's also a walkthrough that can help you understand:

What interviewers are really looking for in your answers.
What types of examples you can pull from your own experience.
How to answer honestly, even when you don’t have the exact experience they ask about.
How to practice your answers without sounding rehearsed.

Want an easier way to keep track of everything?

I created a FREE Notion template where you can write your answers, track your prep, and feel a little more ready for the big day.

The link’s at the end of this post, so scroll down to grab it.

Types of Interview Questions

Before we get into the actual questions you might receive, it helps to know that there are a few different types you’ll run into in a data analyst interview.

Some are technical, where they’ll ask about SQL, dashboards, other tools the job requires, or generic questions like how to clean a dataset. Others are behavioral, like how you work with stakeholders or handle feedback. Then there are business logic questions that test how you think through real business problems, and sometimes broader ones about your goals or experience.

Knowing what type of question you’re answering helps you figure out what they’re really looking for, and how to tailor your response without rambling.

How to Prepare

One of the best things I did when I started prepping was to sit down and write out a few real examples from my past roles. I don't know about you, but I had a lot of work experience that I couldn't remember until I started writing them down. And they don't need to be all data analytics experience (especially if you haven't had a role in the field before); just any experiences from your work or personal projects that you can use to answer some questions on your skillset and problem-solving abilities. For example, times when you solved a problem, fixed a process, helped a team understand something, or figured something out on your own.

You also don’t need a unique story for every question. Most interview questions — whether technical, behavioral, or business-focused — can be answered using the same 3 to 4 examples if you frame them right. That's where the practicing comes in.

Formatting your answers is also important. You don't just want to tell them what you did, you need to explain the outcome. You can do this using the STAR format, especially for behavioral or open-ended questions.

If you haven’t heard of it, STAR just stands for: Situation, Task, Action, Result.

You don’t need to follow it rigidly, but it’s a useful way to structure your answers so you don’t ramble or get stuck mid-story.

Focus on:

What was going on
What you were responsible for
What you did
And what came out of it

You can keep it short and simple, but having a mental structure makes it easier to answer questions clearly, especially under pressure. The "Result" part of the STAR method is the part that people tend to forget, so think of it as the "so what?" part of your answer. You told them about the situation and what happened, so you need to end with what happened and what you learned.

Here’s what helped me get ready for my first interview:

Write down a few projects or tasks you’ve worked on, like where you used SQL, built a report, worked with stakeholders, or solved a problem with data.
Practice saying them out loud so you’re not struggling too much during the interview. Even if you know what you want to say, it sounds different in your head than it does when you speak it. This helps a lot if you are anxious like me, especially if you tend to have a shaky voice when you get nervous.
Time yourself. Most good answers fall in the 1–2 minute range. If you go longer, you risk losing the point, and you don't want the interview to get cut short. Too short (and speaking too fast) means you're not providing enough detail, and you want to make sure you put your best foot forward.
Don’t try to memorize full answers. Instead, jot down keywords or bullet points that help you remember your examples without sounding scripted. The more you practice, the easier it will be to speak about your experience in a natural way.
Record yourself answering a few questions and listen back. You’ll catch what sounds awkward, repetitive, or unclear. If you tend to use a lot of filler words like "um" or "like," you will notice that. You'll also notice if you are speaking too slow or fast, or if your voice gets shaky.
Prepare for questions you don’t know how to answer. This happens to everyone. The important thing in these situations is to stay calm, be honest, and show them how you’d approach the scenario using the experience you do have. Don't pretend you have the experience or knowledge if you don't. Take a few seconds to think, then respond. Don't panic!

The more comfortable you are with your own experience, the easier it is to answer any question, even the ones you haven’t seen before.

Type 1: General and Icebreaker Questions

Tell me about yourself

This is often the first question you will get asked, in some form or another. It's a way to make you feel more comfortable, because talking about yourself is not as stressful. Just be yourself and have something prepared but not memorized. This is your elevator pitch and it needs to be concise and focused, so keep it between 1 to 2 minutes in length.

IMPORTANT NOTE: This isn’t your life story.

The goal is to give them a quick summary of your path into analytics and what you’re looking for now.

If you’re newer to data, talk about the moment you started getting into it. Maybe you worked in a field where you ended up being the one fixing spreadsheets or pulling reports.

If you’ve been in data roles already, highlight how your skills have grown. Maybe you started with Excel and now use SQL daily, or you've moved from building dashboards to automating recurring reports.

The key is to connect the dots between your background and why you're sitting in front of them now.

And do not recite your resume word-for-word. They have your resume already, and you made it to the interview, so now is your time to showcase why you would be a good fit.

Why did you get into data analytics?

You don’t need a profound story or moment of wisdom. Just explain a moment or series of events that got you interested in the field.

For some people, it’s solving a business problem with spreadsheets. For others, it was using data to catch an error that no one else noticed. You might’ve realized you enjoy cleaning messy data, spotting patterns, or presenting numbers in a way that finally made sense to others. Maybe you just like spreadsheets.

If you transitioned into data from another role, what pulled you into the analytics side? That’s what they want to hear.

Why are you looking to leave your current role?

This one can trip people up. Keep your answer future-focused, not about complaints. This is not the time to talk about a manager you didn't get along with or a toxic work environment.

A good response often comes from wanting to do more. Maybe you want a role where you have can have more ownership, more technical work, more strategic analysis. If your current role is heavily ad-hoc based, or based on manual reporting, that’s something you can mention. If you're looking for a team that works with more modern tools, or where data plays a bigger role in decision-making, say that.

Frame it around your desire to grow and contribute in a new way, not what's lacking where you are. Keep the conversation positive.

What are your strengths?

Think about what you’re known for at work, and what people reach out to you for. Maybe the company requires a specific tool in the role that you have a lot of experience with, or knowledge in a certain industry. Those are your strengths.

Other examples could be your ability to clean messy data and make sense of it quickly. Or maybe you’re good at simplifying complicated dashboards so non-technical users can actually use them. Maybe you're the one who double-checks everything before it goes out.

Use a real example from a time you made something clearer, faster, or easier. That’s a strength worth mentioning.

What’s are your weaknesses?

This is another tricky one. Please don't say you're a perfectionist. It is overused and sounds fake.

Pick something real but fixable. Maybe you haven’t worked with a certain tool that’s common in the industry or mentioned in the job description, like Python, Power BI, or even public speaking. Or maybe you tend to overthink before speaking up in meetings.

What matters is showing that you’ve recognized it and are actively working on it, by taking courses, asking more questions, or just practicing in small ways at work.

What are you most proud of in your data work?

This question actually tripped me up the first time I was asked. I wasn't prepared at all for what I might be most proud of. Luckily, I had just finished working on a huge project that was complex and had a lot of moving parts, so I was able to use that.

If you are asked this question, you don't need to provide a massive project as your answer. What you want to focus on here is impact.

Maybe you built a dashboard that helped with a major business decision. Or maybe you figured out a logic issue in a report that saved someone from presenting the wrong data. It could even be a moment where you made something easier to understand.

Even a small fix can be worth mentioning if it had an impact on someone.

How do you stay current or keep learning?

Data (and tech in general) is one of those fields where there are new tools and new processes coming out every day. They want to know if you can keep up.

If you’ve taken online courses, list one or two, especially if it’s on a topic you haven’t used much at work. If you follow any content creators or read articles on SQL or data visualization, mention that. Even if you learn by experimenting with your own data (Google Sheets, Power BI, Kaggle), that counts.

They want to see that you're naturally curious and motivated to keep learning.

Type 2: Behavioral Questions

These are the kinds of questions where they can find out how you work with others and handle problems.

Tell me about a time you solved a problem with data

Think of a time when someone had a question and you were able to find the answer in the data.

For example:

A drop in performance you investigated.
A team making decisions without tracking the right metric.
A request that seemed confusing until you pulled the right dataset.

The goal is to walk them through your approach: how you got the data, what you looked for, and what changed because of it.

Describe a time when you had a tight deadline and how did you handle it?

Everyone’s had this experience at some point. Think of a report, dashboard, or presentation where the turnaround time was unrealistic.

Maybe someone asked for a dashboard for a meeting that same day. Or you got pulled into a request hours before the deadline. Share how you prioritized, communicated, and delivered a version that was “good enough” to meet the deadline, even if you made improvements afterward.

This shows your ability to work under pressure without panicking.

Have you ever explained something technical to someone non-technical?

You probably have, even if you didn’t think of it that way.

Think about a time when you walked a team member or stakeholder through a chart, metric, or SQL logic. Did they misunderstand a KPI? Were they confused by a table filter or dashboard layout? That’s your example.

Mention how you adjusted your language, used visuals, or clarified terms. If they walked away with a better understanding, that’s exactly what the interviewer wants to hear.

Tell me about a time a stakeholder changed their requirements halfway through

Think about a time when you were halfway through building a report, dashboard, or even just pulling data, and the goal suddenly changed. Maybe the metric they wanted wasn’t defined yet, or they realized they needed to filter by region instead of product, or they gave you the wrong KPI.

The point of this question is to find out what you did:

Did you ask clarifying questions?
Did you explain the trade-offs in scope or timeline?
Did you adapt and still get it done?

The interviewer wants to know you stayed flexible without having everything fall apart.

Give an example of a mistake you made and what you did about it

We’ve all made mistakes, so don't say you've never made one! The thing that matters is how you fixed it.

Maybe you:

Pulled numbers from the wrong table.
Forgot to update a date filter.
Shared a dashboard before double-checking it.

Pick something honest but small enough that it didn’t cause a lot of damage. Then focus on how you caught it (or how it was caught), what you did to fix it, and how you made sure it didn’t happen again (this last part is key!).

How do you handle feedback?

You can’t work in data (or any field) without feedback. That's how you grow.

Use an example where:

Someone asked you to change how a dashboard looked or worked.
A stakeholder misunderstood your analysis and you realized you needed to explain it differently.
A manager suggested a better way to approach your logic.

This shows you’re open to input and care about improving.

How do you prioritize tasks with tight deadlines?

This one is more about your process than the story itself.

You could explain how you:

Check in with stakeholders to confirm priorities.
Break large requests into smaller steps.
Deliver a version sooner and iterate from there.

You can also mention that you’ve learned to ask “what’s needed vs. what’s nice to have.”

If you’ve worked in an environment with multiple teams asking for things at once, that’s a great setting for this answer.

Have you ever had to push back on a request?

You have to prioritize your work at times, so that involves pushing back on requests.

You could mention a time when:

A stakeholder wanted a report that already existed.
They asked for data that wasn’t tracked.
They asked for a report that didn’t make sense based on how the data was structured.

The best examples are ones where you didn’t just say “no,” but helped them find an alternative, like a faster solution, a different metric, or another report that gave them what they needed.

Type 3: SQL and Technical Logic

SQL is an important tool in most data analyst roles, so you may get some questions that test your knowledge of certain topics or functions. They don't only want to know if you have experience with these functions, but also how you explain them.

What’s the difference between INNER JOIN and LEFT JOIN?

Your answer doesn’t need to be textbook-perfect, but make sure you mention that:

INNER JOIN only includes matches from both tables.
LEFT JOIN includes everything from the left, even if there’s no match on the right

You can also mention that:

You’ve used INNER JOIN for when only full matches matter (e.g. orders and customers).
You’ve used LEFT JOIN when you need to see what’s missing (e.g. customers with no purchases).

How do you remove duplicates in SQL?

This question sounds simple but has a few layers to it.

A few things to mention include:

Using ROW_NUMBER() or DISTINCT to keep the latest or first version of a record.
Using GROUP BY if the logic is simple.
Joining to a CTE that filters duplicates out by ID, timestamp, or some rule.

What’s a CASE statement and when would you use it?

This is one of the most useful SQL functions, so they want to hear that you’ve used it in previous roles or projects.

Examples of where CASE might come in:

Creating custom labels like “High / Medium / Low” based on a score.
Mapping values like 1/0 to “Yes” and “No."
Handling NULLs by replacing them with defaults.

You don’t need to explain syntax for this one, just show that you understand why it’s useful in reporting.

How would you find the second highest value in a table?

This is a logic test, not a trick question.

You can mention:

Using ROW_NUMBER(), RANK(), or DENSE_RANK().
Using a subquery to SELECT MAX(salary) where salary < the MAX(salary).

The key is showing that you understand ranking and filtering, and that you’ve dealt with cases like ties or NULLs.

Explain the difference between RANK() and DENSE_RANK()

They want to know you understand how window functions behave. You don’t need to go deep into syntax.

Just explain:

RANK() skips numbers if there’s a tie (1, 2, 2, 4).
DENSE_RANK() doesn’t (1, 2, 2, 3).

This is important when showing things like top 3 performers, where gaps can throw off reporting. If you’ve used either in a report, like ranking customers by spend, that’s a good type of example to bring up.

Type 4: Tools, Dashboards, and Reporting Logic

The tools used in each role can vary a lot, so this is just an example of questions that I have been asked (and you may be asked) in your interviews. It's best to prepare questions specific to the tools used in the job you're interviewing for.

How have you used Excel for analysis?

Even if you're working in SQL or BI tools extensively, Excel always comes up. You want to show you can use Excel to think and troubleshoot when other tools aren’t available

Think about:

Tracking KPIs manually before automating them.
Pivot tables for quick ad hoc summaries.
Using formulas like VLOOKUP, IF, INDEX MATCH, or XLOOKUP.
Cleaning exported reports from other systems.

If you’ve ever created a reconciliation sheet, cleaned inconsistent data entries, or used conditional formatting to flag issues, those are all great examples.

What’s the difference between Power BI and Tableau?

You don’t need to have used both. The interviewer just wants to know that you understand how BI tools work, in general.

If you’ve only used one tool, talk about:

Why you liked it.
How you built your dashboards.
What kinds of problems it solved.

You can also talk about:

Power BI being tightly integrated with Excel and Microsoft tools.
Tableau being strong for more customized visuals.
Both tools allowing data blending, filters, slicers, and interactivity.

If you used a BI platform to build dashboards that helped a team self-serve their metrics or saved you time from repeating reports, that’s the kind of thing they want to hear.

Walk me through how you’d build a dashboard from scratch

This is less about design and more about your thinking.

Start with the context and discussion with your stakeholders before you build a dashboard:

Who is this for?
What decisions are they trying to make?
What data do they care about?

Then mention:

Pulling or requesting the data.
Creating basic metrics and checking the logic.
Sketching layout ideas before jumping into the tool.
Testing filters and usability.
Adding any help text or documentation.

How do you decide which chart to use?

They’re testing if you choose charts effectively based on data type and with the end user in mind.

Think of a time you had to choose:

Line charts for trends over time.
Bar charts for comparisons.
Stacked bar for breakdowns.
Tables when exact numbers matter.
Donuts or pies only when proportions are very limited and simple.

If you've ever redesigned a chart because people were misinterpreting it, that’s a great story to share.

What are the key steps in cleaning a dataset?

Cleaning is a often a huge part of a data analyst's job, and they want to know how you approach it.

You could mention:

Scanning for missing or inconsistent values.
Standardizing formats (dates, currency, text casing).
Removing unnecessary columns.
Fixing typos or mapping category names.
Checking for duplicates and joining issues.
Validating values with filters or spot checks.

If you’ve worked with survey data, customer feedback, or large exports from third-party tools, those are good examples of messy data that needed a clear process.

How do you handle messy or missing data?

This goes a step further than general cleaning. In this question, they want to know how you make judgment calls.

Start with:

Understanding why the data is missing (is it expected, a system issue, user error?).
Whether you can impute it (using averages, historical values, etc.).
Or whether to exclude it at all (if it skews things too much).

Context is the underlying determinator. If you’ve ever flagged bad data to a stakeholder and had to explain what it meant for the results, that’s a great example to mention.

Have you used Power Query or DAX? In what context?

Even if you’ve only done light work with these, it’s worth showing how you used them.

Power Query: You might’ve used it to clean and transform data before loading it into Power BI or Excel. Maybe you've done things like removing blanks, changing column types, or merging tables.

DAX: Think calculated columns and measures like running totals, YTD values, or filtering based on conditions.

If you haven’t used DAX yet, it’s fine to say so, but if you’ve worked with calculated fields in other tools (like Looker, Tableau, or even SQL), mention them as it shows a similar mindset.

You might even get asked what is the most complex function you've used, so prepare an answer for that.

How do you make dashboards useful for non-technical users?

This is all about usability and communication, and it's a critical skill for any data analyst. You're bound to be asked a few questions like this, worded in different ways.

You can talk about:

Keeping visuals simple and clear.
Avoiding jargon or using hover tooltips.
Grouping metrics logically.
Creating guided filters or dropdowns.
Adding help text or documentation on how to read the dashboard or defining the metrics.

Think of a time when someone said “I didn’t know what this means,” and you changed your approach.

Tell me about a time your report helped a team make a decision

You don’t need a big story here, just an example that shows your work was important to a business decision.

Examples:

A sales report that revealed underperformance in a certain region.
A customer churn chart that triggered a process review.
A ticket volume dashboard that helped a team justify hiring more staff.
A finance tracker that led to reducing unnecessary expenses.

You want to show that your report made someone take action or think differently.

What tools have you used for automation or scheduling reports?

They want to know if you’ve moved beyond manual reporting. You don't need Python skills to be able to answer this questions.

You could mention:

Scheduling dashboards in Power BI service.
Using Google Sheets with automatic refreshes.
Looker report schedules to send weekly email digests.
Excel files connected to live databases.
SQL scripts scheduled with a job scheduler or BI tool.
If you do have Python or R skills, mention how you used them.

Even if your automation was just scheduling a PDF email every Monday, talk about why you did it and how it helped. The tool doesn’t matter as much as the thinking behind it.

Type 5: Business Logic and Real-World Scenarios

There’s one last category of questions I didn’t include in this post.

These types of questions vary depending on the role and the industry, but the purpose is usually the same: to see how you think through real problems, troubleshoot, apply data to ambiguous situations, and communicate your logic.

You won’t always get a neatly worded question.

Sometimes it’s just:

"We noticed a drop in activity. What would you look at?"

Or:

"How do you segment users?"

The tough part about these questions is that there is no ‘correct’ answer. You don’t need industry expertise to answer these types of questions, either. You just need to show your approach in a structured way.

I have 20 example questions for this type, but I won't share them in this post. They will be included as part of Brandyn Ewanek's Udemy course:

Python Data Analysis Bootcamp - Pandas, Seaborn and Plotly.

⭐ Exclusive Content

I recently collaborated with Brandyn to create a brand-new interview prep module for his course. I designed it based on real interview questions data analysts actually get. There is no theory, just examples and explanations to help you walk in prepared.

Brandyn is the instructor behind the DataSimple course on Udemy, where he covers Python, pandas, seaborn, and plotly, and now includes the new interview prep module. His course has helped thousands of students build a strong foundation in data analysis, especially those who are self-taught or transitioning into the field.

Brandyn is a data scientist and instructor with a background in finance and a focus on teaching practical Python skills for data analysis. He’s a CFA holder who transitioned into data science by applying his analytical mindset to real-world problems, like building a risk model for a fintech startup.

If you're looking to build core skills and prepare for the job hunt, this is a great place to start.

👉 Check out the course here

You can connect with Brandyn here:

Bringing It All Together (and Notion template)

Interview prep can feel overwhelming, especially when you're switching careers or haven't had an interview in a while. When I was preparing, I kept reading the same advice over and over, and none of it helped me figure out what I’d actually say when someone asked me a question I hadn’t seen before.

What helped most was writing out a few examples from my experience. I thought of projects where I fixed something, found something, or helped someone understand the data better. I wrote them down for reference, then I practiced talking about those stories out loud until they sounded natural and not rehearsed.

Interview prep doesn’t need to feel like cramming for an exam, but it's a skill and it takes practice to get better at. The more interviews you have, the more easier it will be to answer any question that comes your way.

You don’t need to memorize answers. You just need to know your own experience well enough to talk about it clearly. If they ask about something you haven’t done, it’s okay to say you haven’t and just explain how you’d approach it if you had to. That’s what most interviewers are looking for anyway.

And don't end an interview without asking your own questions. It is a two-way conversation, after all. You want to make sure the company is a right fit for you and your goals, as well.

If you're prepping for an interview right now, here are some last bits of advice:

Take a breath, review your examples, and remind yourself that no one expects you to know everything. They just want to see how you think, how you communicate, and how you solve real problems with data.

If you found this post helpful and want more practice with real-world interview questions, check out the rest of the real scenario and business logic questions in Brandyn's Udemy course. You'll get to see some questions that test how you think, troubleshoot, and explain your approach clearly.

Want to keep all your prep in one place?

I created a FREE Notion template so you can write your answers, stay organized, and feel more prepared for interview day.

It’s simple, editable, and yours to make your own.

👉 Grab it here