Job interviews! They’re not everyone’s favorite pastime, and we’ve all experienced hard interviews where you’re caught off guard by a difficult question that you struggle to answer. Interviews are even harder if you’re interviewing for a role in a field that’s fairly new to you, and the probability of being stuck with a difficult question is even higher.
Let’s say you’ve expressed an interest in pursuing a career in data analytics, you’ve taken a course and are now ready to start applying for jobs. How do you ensure you’re not completely out of your depth going into the interview? What are interviewers likely to want to know about you, and how can you prepare accordingly?
With the help of our resident career advisor Danielle, we’ve produced a list of frequently asked interview questions and tips on how to answer them. We can’t guarantee you’ll bag the job, but we can certainly give you the confidence to walk out of the interview room knowing you’ve given it your best. Let’s start the interview!
Data analyst interview questions and answers
1. Introductory Questions
These questions are designed to ease you into the interview, and will focus on broad topics so the interviewer can get to know more about you.
“Tell me about yourself.”
Danielle says: When an interviewer asks this, what they’re essentially saying is: ‘Can you walk me through your career history, giving one takeaway from each of your experiences in work and education?’. It’s important to bear this in mind when fielding this question, and structure your answer accordingly, so that you share the right kind of information and leave out the bits that aren’t important.
“How would you describe yourself as a data analyst?”
Danielle says: This is your chance to impress them with your passion and drive to work in data analytics. You need to press home your love of data, and explain the reasons why you’re pursuing analytics as a career. Lead the interviewer through your journey to becoming a data analyst and your approach to data analysis.
Demonstrate your awareness as to how and why having a solid understanding of the industry you’re looking to work in enhances your ability to carry out effective analysis. Outline your strengths and where they lie. Are you great at collaborating with teams? Are you a natural at programming languages? Do you love giving presentations on your findings? Explain what tools you’re familiar with, such as Excel, and what programming languages you know.
“What do you already know about the business/product—what value does your skill set add to what we’re doing?”
Danielle says: It’s essential you demonstrate your knowledge of the business and product, because that’s a key part of being a data analyst. The art of analytics lies in your ability to ask great questions, and you’ll only be able to ask such questions with sufficient background knowledge in the field. So demonstrate to the interviewer that you’ve done your research, and how your own analytical skills relate to the field. Perhaps you’ve already worked in the area before in a different capacity; show them how your previous experience relates to your new set of skills!
2. Data analysis questions
These questions will focus on your own experience working in data analysis. What’s your background? What have you done before? Make sure you spend time considering your past experience, so that you’re able to immediately bring up examples when needed.
“Please share some past data analysis work you’ve done—and tell me about your most recent data analysis project.”
Danielle says: It’s best to use the STAR method when asked a question such as this: Situation, Task, Action, Result. Outline the circumstances surrounding a previous data analysis project, describe what you had to do, how you did it, and the outcome of your work. Don’t worry about being fairly rigid in your approach to this answer—just make sure the interviewer has everything they need to know by the end.
“Tell me about a time when you ran an analysis on the wrong set of data and how did you discover your mistake?”
Danielle says: The most important thing when answering questions regarding a mistake or a weakness, is to acknowledge ownership over what’s happened. Mistakes aren’t important to the interviewer, but your transparency and how you found a solution is. Outline the learning process and how that’s enabled you to work more effectively.
“What was your most difficult data analyst project? What problem did you solve and what did you contribute to make it a success?”
Danielle says: Provide some context for what you’re about to say. Explain the project and the goal of it, going into some detail about your own role in the process. Then explain what aspect of it you found the most difficult. Your solution to overcoming this difficulty is what the interviewer’s looking for.
3. Technical Questions
These questions will touch upon more technical aspects of the role of data analyst. Be prepared to bring up more working examples from your previous roles, and make sure you’ve prepared an answer for what aspects of the role appeal to you. Don’t worry though, these questions aren’t going to dive too deep into your expertise—so don’t worry about being put on the spot!
“What’s your favourite tool for data analysis—your likes, dislikes, and why? What querying languages do you know?”
Danielle says: For this question, It’s important you detail your (hopefully excellent!) Excel skills, which are an integral part of performing data analysis. Prove your Excel credentials, outlining any courses you’ve been on or examples of analysis you’ve performed with the program. Employers will also want to know what querying languages you’re familiar with, whether it be SAS, R, Python or another language. Querying languages are used for larger sets of data, so you’ll need to prove you have a solid foundation in one of these languages. Here’s a top tip: try and find out what querying language the company you’re applying to uses, that might come in handy!
“What do you do to stay up to date with new technologies?”
Danielle says: In data analytics, staying on top of developments in the field usually involves keeping your knowledge of existing libraries and frameworks up to date. So make sure you’re able to bring up some names of libraries when asked. The Kaggle Community is an online resource for data scientists and analysts that contains a huge amount of information on the subject, so why not join the community and expand your knowledge. Name dropping such resources in an interview can sometimes help demonstrate your passion for data analytics!
“What are some of your best practices to ensure that you perform good, accurate, and informative data analysis?”
Danielle says: You’re generally going to be referring to data cleansing checks when answering this question with regard to data analytics. By undertaking such checks, you’re able to ensure results are reliable and accurate. Explaining to your interviewer that an awareness for the kind of results that would be implausible is also a good thing to do. The interviewer might give you a small logic problem and ask you to explain how you’d overcome it. Explaining what you’d do and the necessary investigations you’d undertake if something looks odd will tell the interviewer that you have a good problem solving mindset.
“How do you know you’re analyzing the right data?”
Danielle says: Asking the right questions is essential to being a good data analyst, so every new project must begin with asking the right questions. You need to ensure you’re measuring what needs to be measured, so walk the interviewer through your processes of determining what data needs to be analysed to answer the question.
“Tell me about a time that you had to explain the results of your analysis to stakeholders.”
Danielle says: This is a communications skills question—the interviewer is looking for evidence of your presentation skills. Explain times when you’ve had to present data you’ve worked on. Talk about how you’ve justified the results, and what impact your results had on the project.
4. Wrap-up questions
These questions tend to be hard to answer, but it’s very important to prepare well for them. You need to leave a good lasting impression with the interviewer!
“Tell me about your professional development goals and how you plan to achieve them?”
Danielle says: This is another way of saying ‘where do you see yourself in five years?’ It’s always hard to answer this question! Outline the next set of skills and tools you want to learn, or explain what leadership responsibilities interest you. Differentiate whether you want to go down the subject matter track, or the leadership track. Do you want to have a mentor, or eventually be a mentor yourself? Is there a pivot you want to take in your career? Or do you see yourself growing into the role of data scientist, or specializing more in programming? You’ll impress the interviewer if your future career objectives are clear.
“Do you have any questions?”
Danielle says: It’s a good idea to prepare three to five questions in advance of the interview. If you’re going to be interviewed by several people, then prepare more. You want to avoid having your questions already answered during the interview, so aim to have a surplus. Avoid generic questions such as ‘where do you see the company going?’ and personalize your questions to the interviewer. This is the part of the interview where you get the opportunity to open a dialogue and show the value you can bring to the company, if you haven’t already. Questions such as ‘Who will I be most closely working with?’ and ‘What are the biggest challenges facing the team this year?’ are likely to leave a good impression on your interviewer.
You’ll now have a greater understanding of the kind of questions you’ll be asked in interviews for data analyst positions. If you’re curious about becoming a data analyst, why not take our one-month Intro to Data Analytics course? You’ll come away with a solid grounding in Microsoft Excel, one of the key tools used by data analysts. Not ready to commit to a full course? Try this free, five-day data analytics short course.
If you’d like to read more about working as a data analyst, we suggest you read the following articles: