Over 2.5 quintillion bytes of data are created every day, from searches to Netflix streams to splitting your dinner on Venmo. And since the amount of data in the world has nowhere to go but up, there will no doubt be a continued need for data analysts to pull it all together.
In fact, data analyst jobs on Indeed have increased by 17% from October 2017 to October 2019. But something to remember: While the need for data analysts is climbing, properly preparing for interviews—and impressing decision makers—is the only sure way to land your next opportunity.
That said, what kind of data analyst interview questions can you expect in your search? What topics should you expect? And how will you communicate that you have the right skills the company needs to make better business decisions? To put you on the right path, we’re covering three types of data analyst interview questions, plus example questions for each.
Data analyst interview questions you need to know
According to Mitchell Breinholt, data analytics team lead at Seen by Indeed, there are generally three types of interview questions you may encounter. Each topic helps the interviewer understand how you’ll contribute to the role, from your communication and problem-solving abilities to your knowledge and application of data analysis tools and techniques.
Data analyst interview questions will vary from company to company. But they’ll also differ based on the type of work you’ll be doing—i.e., does the company want a data analyst with a strong business sense, or one that leans heavier on data science? Both the job description and types of interview questions you’re asked will clue you in, so listen closely (and remember to ask questions, too).
Questions related to how data fits into business
Any data analyst role leans heavily on the use of technology, but your soft skills (how you perform and interact with others) are just as valuable. And when it comes down to who gets the job—you or another equally tech-savvy candidate—your soft skills can be what puts you on top.
A few of the most valuable soft skills to have as a data analyst? Problem-solving, critical thinking, collaboration and communication.
You should be able to collaborate across technical and non-technical functions (e.g., marketing, sales, operations) as well as logically break down big-picture problems into actionable steps. Hiring managers will also want to know that you can effectively communicate to stakeholders in a way that builds trust and confidence—i.e., will leaders in the company be comfortable approaching you with high-level challenges that impact business growth?
How to prepare
Many data analyst interview questions provide an opportunity to highlight your soft skills, such as questions about how you overcame a challenge (e.g., managing pressing deadlines), uncovered possible reasons behind a dip in company growth or handled a presentation that went south.
Before your interview, think about how you’ve navigated these types of situations and be able to share those examples in detail, from your thought process to the final outcome.
- How do you explain technical information to a non-technical audience?
- Describe your experience presenting reports and insights to leadership or stakeholders.
- How do you approach solving for a high-level problem, like a sudden spike in customer churn?
- Tell me about a project that went wrong. What happened and why? What steps did you take to avoid repeating the issue?
- Talk about a time you had to influence or persuade others. Were you successful?
Questions related to technical skills
As a data analyst, your primary goal is to provide meaningful insights that help drive the business forward. You’ll work with datasets to uncover trends and patterns, from user logins to sales figures. And beyond gathering, inputting and organizing this information within spreadsheets and databases, you’ll also be called on to build out data visualizations (e.g., dashboards, charts, graphs) so that you can present your findings to company leadership and stakeholders.
All of this requires the right blend of tech skills. And to really wow the hiring manager, you’ll want to sharpen your knowledge of the ones in highest demand. Our data shows that SQL, Stata and Microsoft Excel are high up on the list of skills employers want in a data analyst, followed closely by other data visualization and statistical software (Tableau, SAS) and programming languages (Python, R).
Machine learning tops the list of the most in-demand data analyst skills, but only 3% of data analyst jobs include it. This suggests that it’s more of a bonus skills, not a must-have. Regardless, it’s a method to understand if you want to increase your marketability as a data analyst.
How to prepare
Get comfortable describing how you’ve used these technologies and your level of expertise for each. Replay specific instances where you’ve used data analysis tools to solve for challenges—or avoid challenges altogether. Find out what technologies the company uses (typically listed out in the job description) so you can detail your experience with those in particular.
- What is your experience working with programming languages like Python and R?
- How do you monitor and measure the effectiveness of newly implemented processes?
- How have you improved team efficiency by replacing manual processes with automation?
- How are you currently developing your technical skill set?
Questions related to data analysis processes
The complete data analysis process can be broken down into several steps, which may vary based on the role, company and project needs. It generally starts with defining the objective, which leads into collecting, cleaning and analyzing data, then onto data visualization and communicating the findings.
But digging into a real-world problem isn’t as straightforward as step one, step two, step three. And because data analysts are often key to uncovering trends that will determine business decisions—and shape the future of the company—show the interviewer you can think logically about what steps to take (or not take).
Part of quickly finding the root cause of any problem is knowing how to approach it from the start. When you’re assigned a new project, how do you begin? Do you understand how to adjust your process based on changing business needs? Having a solid data analysis process in place also means asking and answering the right questions along the way (e.g., what happened, why did it happen, what might happen, what action should be taken).
How to prepare
Much of what interviewers want to know tie into both your thought and problem-solving processes. In addition to looking for how you tackle a project off the bat, they want to understand how you break it down, determine what’s the most important data to uncover and navigate unforeseen roadblocks.
Know your own processes and approach. Prepare examples of times you gathered and cleaned the right data that fueled a critical business decision and brought immediate results. Connect the dots between your work and the impact of your work (e.g., saved 30% in annual company expenses) to not only prove your value, but that you understand how your role plays into organizational goals.
- What project are you most proud of and why?
- How do you deal with dirty data?
- What’s the largest dataset you’ve worked with? What was the project?
- How often do you retrain a data model?
- How do you choose what data to pull or when you’ve collected enough data to build a model?
Prepping for data analyst interview questions like a pro
Although this isn’t a complete list of what you’ll be asked, it’s a good starting point that’ll help you prepare for the big day. And to really drive home that you’ll be a solid team addition (and will put your skills to use ASAP), always go into an interview with specific examples of how you’ve overcome challenges in the past, plus how you embrace a growth mindset for a successful future.
Remember, data analyst roles aren’t the same for every company, so read the job posting thoroughly to better understand what the company is looking for. Make sure the role aligns with your skills and goals, prepare for common data analyst interview questions and get ready to land your next big opportunity.
*Methodology: Indeed analyzed the percentage change in the share of job postings with “data analyst” in the job title over a two-year period from October 2017 to October 2019.