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Top interview questions for data analysts
What makes a good data analyst—and how do you spot them? These interview questions will help you identify ability and prowess in your next data analyst hire. Here, you’ll find questions to help assess a candidate’s hard skills, behavioral intelligence, and soft skills.
Use these questions to identify a candidate’s technical knowledge and abilities
Use these questions to determine how a candidate handled situations in the past
Use these questions to assess a candidate’s personal traits and cognitive skills
Top hard skills interview questions for data analysts
What statistical analysis tools and database software have you previously used? What are your favorites and why?
Why this matters:
Data analysts work with a variety of different tools and programs, and new ones are rolled out every year. Depending on the role, you may want to hear different things from the candidate’s answer. But what’s most important is their ability to learn and adapt, as this will help your company stay on the cutting edge of data analysis.
What to listen for:
- Every data analyst candidate should know the current dominant data language, SQL.
- Listen for references to other relevant database concepts and business intelligence (BI) tools as well, such as SPSS or SAS.
- Look for a willingness to learn new software and tools if needed.
How would you go about measuring the business performance of our company, and what information do you think would be most important to consider?
Why this matters:
While every interviewee will hopefully research your company thoroughly before speaking with you, a top data analyst also knows how to ask good questions to understand the business’s needs. This question can also provide a window into the sort of projects they’d be eager and able to tackle for your company if hired.
What to listen for:
- Listen for answers that show the candidate did their research and has a good sense of your company’s goals.
- Look for signs that the candidate can adopt a business mindset and is familiar with your industry’s practices and norms.
What are some best practices for data cleaning? What are the steps you take?
Why this matters:
Data cleaning is an essential process that enhances the quality of data. Top data analysts understand this and never skip this step. When unclean data is used for analysis, it can lead to misleading results and wasted time, which is why data analysts must ensure the integrity of all data they use for analysis purposes.
What to listen for:
- Candidates should list best practices they follow, like sorting the data by different attributes or breaking a large dataset down to increase iteration speed.
- A strong answer will include details about why they favor particular practices, based on their own experience.
Top behavioral questions to ask data analysts
Tell me about a time when you think you demonstrated good data sense.
Why this matters:
A data analyst with good “data sense” can look at a chart or table and sense that something is off—say, a conversion rate that’s too low, or a booking number that seems wrong. Untangling data issues like this can come from experience, but also from a deep understanding and talent for statistics, business, and economics.
What to listen for:
- Great candidates will be able to provide a detailed account of how they identified an inconsistency and worked through the appropriate channels to solve it.
- Answers should demonstrate data savviness and strong problem-solving skills.
Describe your most complex data project from start to finish. What were the most difficult challenges, and how did you handle them?
Why this matters:
Having the candidate talk you through a large-scale assignment will give you a good insight into their overall experience level as a data analyst. If they’ve taken a project from conception to actionable results—and worked through complex data challenges to get there—then this is an experienced data analyst worth moving to the next round.
What to listen for:
- Answers should indicate that the candidate has a strong iterative process for tackling challenging projects.
- Their answer should make clear that feedback from the stakeholders plays a critical role in their process.
Tell me about a time when you designed an experiment. How did you measure success?
Why this matters:
Data analysts will often conduct experiments to determine how successful a campaign or feature will be. This allows them to help direct the company’s strategy, often leading to better results. Depending on the candidate’s level of experience, they may not have conducted advanced experiments, but should understand the logic behind them.
What to listen for:
- Candidates should be able to clearly explain the objective of the experiment, like a simple A/B test to decide which of two campaigns to roll out on a wider scale.
- Strong answers will also cover the metrics they used to track and quantify the results, and why.
How to screen data analysts for soft skills
What do you think are the three best qualities that great data analysts share?
Why this matters:
Great data analysts rely on their soft skills as much as their technical abilities. This question can help you assess which soft skills candidates value most in others—and how they see themselves. This will give you a good sense of how they’ll fit in with the rest of the team and whether they’ll bring something new to the table.
What to listen for:
- Candidate should demonstrate an understanding that a well-rounded analyst is more than just a number cruncher.
- A strong answer may mention traits like problem-solving and analytical thinking, coupled with the ability to get along well with others.
How would you explain your findings and processes to an audience who might not know what a data analyst does?
Why this matters:
Since data analysts work with staff with varying levels of data fluency, you need to know they can communicate effectively. This is a role that often requires working directly with management, engineers, and end users to gather assignments and requirements, provide detailed status updates for large data projects, and build communication bridges across departments.
What to listen for:
- Look for signs that the candidate is passionate about helping others understand data and can express their ideas clearly to aid comprehension.
- Answers should indicate that they’re accustomed to formatting data in an easy-to-read manner.
What drew you to data analysis as a career?
Why this matters:
Asking this question can help you spot candidates with passion and ambition. It can also help you understand where their interest lies and whether the role will be fulfilling for them. If they are deeply passionate about data and your company can offer the kind of analysis opportunities that they crave, they’re likely to thrive in the role.
What to listen for:
- Look for candidates who show a genuine interest in statistics, economics, logic, and reasoning.
- Great answers may touch upon exciting developments in the field, such as the rise of Big Data and artificial intelligence.
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