Why you should be familiar with data analyst interview questions?
If you’re aiming for a data analyst job, sooner or later, you’ll reach the final stage of the application process - the data analyst job interview. So, how can you ace the interview with ease? By being well-familiar with the data analyst interview questions in advance.
And that’s exactly why you should read this article. Here you’ll learn everything you need to nail the challenging job interview and secure a career as a data analyst:
- How to prepare for the data analyst interview (the top data analyst skills you need to acquire);
- A list of real-life data analyst interview questions and answers;
- What the data analyst interview process in 3 leading companies looks like.
Bonus content: how to present yourself in your best light and leave a lasting impression on the interviewers. But that will come last. In the meantime…
How to Be Ready for a Data Analyst Interview?
No matter where you apply for a data analyst job, no recruiter will call you in for an interview, if you don’t possess the necessary skills. And when it comes to data analysis, you can’t go without the following:
- Programming and coding language skills using Python, R, etc.;
- Expertise in SQL and a good understanding of how relational database management systems work;
- Tableau Experience with large data sets and distributed computing;
- Excellent Excel skills and ability to use advanced analytics and formulas;
- Knowledge of statistics and statistical software packages, quantitative methods, confidence intervals, sampling and test/control cells.
And while we’re at it, if you want to pursue a career in data analysis but you lack the technical education and skills, we also offer a free preview version of the Data Science Program. You’ll receive 12 hours of beginner to advanced content for free. It’s a great way to check if the program is right for you.
Data Analyst Interview Questions You Should Know the Answers To
General Data Analyst Interview Questions
General data analyst interview questions are not just about your background and work experience. In fact, interviewers might surprise you with questions requiring details about the projects you’ve been involved, and how you approach complex data sets. So, let’s take a look:
1. Can you share details about the largest data set you’ve worked with? How many entries and variables did the data set comprise? What kind of data was included?
How to Answer
Working with large datasets and dealing with a substantial number of variables and columns is important for a lot of hiring managers. When answering the question, you don’t have to reveal background information about the project or how you managed each stage. Focus on the size and type of data.
“I believe the largest data set I’ve worked with was within a joint software development project. The data set comprised more than a million records and 600-700 variables. My team and I had to work with Marketing data which we later loaded into an analytical tool to perform EDA.”
2. In your role as a data analyst, have you ever recommend a switch to different processes or tools? What was the result of your recommendation?
How to Answer
For hiring managers, it’s important that they pick a data analyst who is not only knowledgeable but also confident enough to initiate a change that would improve the company’s status quo. When talking about the recommendation you made, give as many details as possible, including your reasoning behind it. Even if the recommendation you made was not implemented, it still demonstrates that you’re driven and you strive for improvement.
“Although data from non-technical departments is usually handled by data analysts, I’ve worked for a company where colleagues who were not on the data analysis side had access to data. This brought on many cases of misinterpreted data that caused significant damage to the overall company strategy. I gathered examples and pointed out that working with data dictionaries can actually do more harm than good. I recommended that my coworkers depend on data analysts for data access. Once we implemented my recommendation, the cases misinterpreted data dropped drastically.”
3. How would you assess your writing skills? When do you use written form of communication in your role as a data analyst?
How to Answer
Working with numbers is not the only aspect of a data analyst job. Data analysts also need strong writing skills, so they can present the results of their analysis to management and stakeholders efficiently. If you think you are not the greatest data “storyteller”, make sure you’re making efforts in that direction, e.g. through additional training.
“Over time, I’ve had plenty of opportunities to enhance my writing skills, be it through email communication with coworkers, or through writing analytical project summaries for the upper management. I believe I can interpret data in a clear and succinct manner. However, I’m constantly looking for ways to improve my writing skills even further.”
4. Have you ever used both quantitative and qualitative data within the same project?
How to Answer
To conduct a meaningful analysis, data analysts must use both the quantitative and qualitative data available to them. In surveys, there are both quantitative and qualitative questions, so merging those 2 types of data presents no challenge whatsoever. In other cases, though, a data analyst must use creativity to find matching qualitative data. That said, when answering this question, talk about the project where the most creative thinking was required.
“In my experience, I’ve performed a few analyses where I had qualitative survey data at my disposal. However, I realized I can actually enhance the validity of my recommendations by also implementing valuable data from external survey sources. So, for a product development project, I used qualitative data provided by our distributors, and it yielded great results.”
5. What is your experience in conducting presentations to various audiences?
How to Answer
Strong presentation skills are extremely valuable for any data analyst. Employers are looking for candidates who not only possess brilliant analytical skills, but also have the confidence and eloquence to present their results to different audiences, including upper-level management and executives, and non-technical coworkers. So, when talking about the audiences you’ve presented to, make sure you mention the following:
- Size of the audience;
- Whether it included executives;
- Departments and background of the audience;
- Whether the presentation was in person or remote, as the latter can be very challenging.
"In my role as a Data Analyst, I have presented to various audiences made up of coworkers and clients with differing backgrounds. I’ve given presentation to both small and larger groups. I believe the largest so far has been around 30 people, mostly colleagues from non-technical departments. All of these presentations were conducted in person, except for 1 which was remote via video conference call with senior management.”
6. Have you worked in an industry similar to ours?
How to Answer
This is a pretty straightforward question, aiming to assess if you have industry-specific skills and experience. Even if you don’t, make sure you’ve prepared an answer in advance where you explain how you can apply your background skills from a different field to the benefit of the company.
“As a data analyst with financial background, I can say there are a few similarities between this industry and healthcare. I think the most prominent one is data security. Both industries utilize highly sensitive personal data that must be kept secure and confidential. This leads to 2 things: more restricted access to data, and, consequently, more time to complete its analysis. This has taught me to be more time efficient when it comes to passing through all the security. Moreover, I learned how important it is to clearly state the reasons behind requiring certain data for my analysis.”
7. Have you earned any certifications to boost your career opportunities as a Data Analyst?
How to Answer
Hiring managers appreciate a candidate who is serious about advancing their career options through additional qualifications. Certificates prove that you have put in the effort to master new skills and knowledge of the latest analytical tools and subjects. While answering the question, list the certificates you have acquired and briefly explain how they’ve helped you boost your data analyst career. If you haven’t earned any certifications so far, make sure you mention the ones you’d like to work towards and why.
“I’m always looking for ways to upgrade my analytics skillset. This is why I recently earned a certification in Customer Analytics in Python. The training and requirements to finish it really helped me sharpen my skills in analyzing customer data and predicting the purchase behavior of clients.”
Technical Data Analyst Interview Questions
Technical data analyst interview questions are focused on assessing your proficiency in analytical software, visualization tools, and scripting languages, such as SQL and Python. Depending on the specifics of the job, you might be requested to answer some more advanced statistical questions, too. Here are some real-world examples:
8. What tools or software do you prefer using in the various phases of data analysis and why?
How to Answer
Although you might think you should have experience with as many tools as possible to ace this question, this is not the case. Each company uses specific data analysis tools, so it’s normal that your expertise is limited to those. Of course, if you have worked for a large number of companies, you’re bound to have exposure to a wider variety of analytical software. That said, the interviewer would like to know which tools you feel comfortable with, rather than the number of tools you’ve utilized.
“When it comes to data analysis tools, I can say I’m a traditionalist. That’s why, I find Microsoft Excel and Microsoft Access most useful. I feel truly comfortable working with those, and they’re available in almost every company out there. Moreover, you can achieve great results with them with the right training.”
9. Have you ever created or worked with statistical models? If so, please describe how you’ve used it to solve a business task.
How to Answer
As a data analyst, you don’t specifically need experience with statistical models, unless it’s required for the job you’re applying for. If you haven’t been involved in building, using, or maintaining statistical models, be open about it and mention any knowledge or partial experience you may have.
“Being a data analyst, I can’t say I’ve had direct experience building statistical models. However, I’ve helped the statistical department by making sure they have access to the proper data and analyzing it. The model in question was built with the purpose of identifying the customers who were most inclined to buy additional products and predicting when they were most likely to make that decision. My job was to establish the appropriate variables used in the model and assess its performance once it was ready.”
10. Which step of a data analysis project do you enjoy the most?
How to Answer
It's normal for a data analyst to have preferences of certain tasks over others. However, you’ll most probably be expected to deal with all steps of a project – from querying and cleaning, through analyzing, to communicating findings. So, make sure you don’t show antipathy to any of the above. Instead, use this question to highlight your strengths. Just focus on the task you like performing the most and explain why it’s your favorite.
"If I had to select one step as a favorite, it would be analyzing the data. I enjoy developing a variety of hypotheses and searching for evidence to support or refute them. Sometimes, while following my analytical plan, I have stumbled upon interesting and unexpected learnings from the data. I believe there is always something to be learned from the data, whether big or small, that will help me in future analytical projects."
11. What’s your knowledge of statistics and how have you used it in your work as a data analyst?
How to Answer
Data analysts should have basic statistics knowledge and experience. That means you should be comfortable with calculating mean, median and mode, as well as conducting significance testing. In addition, as a data analyst, you must be able to interpret the above in connection to the business. If a higher level of statistics is required, it will be listed in the job description.
“In my line of work, I’ve used basic statistics – mostly calculated the mean and standard variances, as well as significance testing. The latter helped me determine the statistical significance of measurement differences between two populations for a project. I’ve also determined the relationship between 2 variables in a data set, working with correlation coefficients.”
12. What scripting languages have you used in your projects as a data analyst? Which one you you’d say you like best?
How to Answer
Most large companies work with numerous scripting languages. So, a good command of more than one is definitely a plus. Nevertheless, if you aren’t well familiar with the main language used by the company you apply at, you can still make a good impression. Demonstrate enthusiasm to expand your knowledge, and point out that your fluency in other scripting languages gives you a solid foundation for learning new ones.
“I’m most confident in using SQL, since that’s the language I’ve worked with throughout my Data Analyst experience. I also have a basic understanding of Python and have recently enrolled in a Python Programming course to sharpen my skills. So far, I’ve discovered that my expertise in SQL helps me advance in Python with ease.”
By the way, if you’re finding this answer useful, consider sharing this article, so others can benefit from it, too. Helping fellow aspiring data analysts reach their goals is one of the things that make the data science community special.
13. How many years of SQL programming experience do you have? In your latest job, how many of your analytical projects involved using SQL?
How to Answer
SQL is considered as one of the easiest scripting languages to learn. So, if you want to be competitive on the job market as a Data Analyst, you should be able to demonstrate excellent command of SQL. Even if you don’t have many years of experience, highlight how your skills have improved with each new project.
“I’ve used SQL in at least 80% of my projects over a period of 5 years. Of course, I’ve also turned to other programming languages for the different phases of my projects. But, all in all, it’s SQL that I’ve utilized the most and consider the best for most of my data analyst tasks.”
14. Which Excel functions have you used on a regular basis so far? Can you describe in detail how you’ve used Excel as an analytical tool in your projects?
How to Answer
If you are an Excel expert, it would be difficult to list all the functions you have experience using. Instead, concentrate on highlighting the more difficult ones, particularly statistical functions. If you have experience utilizing the more challenging functions, hiring managers will presume you have experience using the more basic ones. Be sure to highlight your pivot table skills, as well as your ability to create graphs in Excel. If you have not attained these skills yet, it is worthwhile to invest in training to learn them.
If you’re an Excel pro, there is no need to recite each and every function you’ve used. Instead, highlight your advanced Excel skills, such as working with statistical functions, pivot tables, and graphs. Of course, if you lack the experience, it’s worth considering a specialized Excel training that will help you build a competitive skillset.
“I think I’ve used Excel every day of my data analyst career in every single phase of my analytical projects. For example, I’ve checked, cleaned, and analyzed data sets using Pivot tables. I’ve also turned to statistical functions to calculate standard deviations, correlation coefficients, and others. Not to mention that the Excel graphing function is great for developing visual summaries of the data. As a case in point, I’ve worked with raw data from external vendors in many customer satisfaction surveys. First, I’d use sort functions and pivot tables to ensure the data was clean and loaded properly. In the analysis phase, I’d segment the data with pivot tables and the statistical functions, if necessary. Finally, I’d build tables and graphs for efficient visual representation.”
15. What’s your experience in creating dashboards? Can you share what tools you’ve used for the purpose?
How to Answer
Dashboards are essential for managers, as they visually capture KPIs and metrics and help them track business goals. That said, data analysts are often involved in both building and updating dashboards. Some of the best tools for the purpose are Excel, Tableau, and Power BI (so make sure you’ve got a good command of those). When you talk about your experience, outline the types of data visualizations, and metrics you used in your dashboard.
“In my line of work. I’ve created dashboards related to customer analytics in both Power BI and Excel. That means I used marketing metrics, such as brand awareness, sales, and customer satisfaction. To visualize the data, I operated with pie charts, bar graphs, line graphs, and tables.”
Behavioral Data Analyst Interview Questions
To answer this type of data analyst interview questions with ease, you’ll need to take a walk down memory lane and recall details about how you handled specific challenges in your work with stakeholders, coworkers, or clients. Here’s what we have in mind:
16. As a data analyst, you’ll often work with stakeholders who lack technical background and a deeper understanding of data and databases. Have you ever been in a situation like this and how did you handle this challenge?
How to Answer
Data analysts often face the challenge of communicating findings to coworkers from different departments or senior management with limited understanding of data. This requires excellent skills in interpreting specific terms using non-technical language. Moreover, it also requires extra patience to listen to your coworkers' questions and provide answers in an easy-to-digest way. Show the interviewer that you’re capable of working efficiently with people from different types of background who don’t speak your “language”.
“In my work with stakeholders, it often comes down to the same challenge – facing a question I don’t have the answer to, due to limitations of the gathered data or the structure of the database. In such cases, I analyze the available data to deliver answers to the most closely related questions. Then, I give the stakeholders a basic explanation of the current data limitations and propose the development of a project that would allow us to gather the unavailable data in the future. This shows them that I care about their needs and I’m willing to go the extra mile to provide them with what they need.”
17. Tell me about a time you and your team were surprised by the results of a project.
How to Answer
When starting an analysis, most data analysts have a rough prediction of the outcome rested on findings from previous projects. But there’s always room for surprise, and sometimes the results are completely unexpected. This question gives you a chance to talk about the types of analytical projects you’ve been involved in. Plus, it allows you to demonstrate your excitement about drawing new learnings from your projects. And don’t forget to mention the action you and the stakeholders took as a result of the unexpected outcome.
“While performing routine analysis of a customer database, I was completely surprised to discover a customer subsegment that the company could target with a new suitable product and a relevant message. That presented a great opportunity for additional revenue for the company by utilizing a subset of an existing customer base. Everyone on my team was pleasantly surprised and soon enough we began devising strategies with Product Development to address the needs of this newly discovered subsegment.”
18. Why do you think creativity is important for a data analyst? How have you used creative thinking in your work so far?
How to Answer
A data analyst is usually seen as a professional with a technical background and excellent math and statistical skills. However, even though creativity is not the first data analyst quality that comes to your mind, it’s still important in developing analytical plans and data visualizations, and even finding unorthodox solutions to data issues. That said, provide an answer with examples of your out-of-the-box way of thinking.
“I can say creativity can make all the difference in a data analyst’s work. In my personal experience, it has helped me find intriguing ways to present analysis results to clients. Moreover, it has helped me devise new data checks that identify issues resulting in anomalous results during data analysis.”
19. What are the most important skills a data analyst should possess to work efficiently with team members with various backgrounds, roles, and duties?
How to Answer
When answering this question, keep in mind that the hiring manager would like to hear something different than “communication skills”. Think of an approach you’ve used in your role as a data analyst to improve the quality of work in a cross-functional team.
“I think the role of a data analyst goes beyond explaining technical terms in a non-technical language. I always strive to gain a deeper understanding of the work of my colleagues, so I can bridge my explanation of statistical concepts to the specific parts of the business they deal with, and how these concepts relate to the tasks at hand they need to solve.”
20. In your opinion, which soft skills are essential for a data analyst and why?
How to Answer
Soft skills, a.k.a. non-technical skills are important for working efficiently with others and maintaining a high level of performance. As with most professions, data analysts should be aware of how their behavior and work habits affect the members on their team. Therefore, here you should base your answer on past work experience and highlight an important soft skill you have developed.
“I believe leadership skills are one of the major soft skills a data analyst should develop. The way I understand it, leadership means taking action to guide and help the members on your team. And this doesn’t necessarily mean you have to be in a managerial position. In my line of work, leadership would translate into providing expert insights regarding company data and its interpretation. That’s a skill I’ve worked hard to develop over the years. I can say being confident in my abilities has now established me as a leading figure in my area, and my team members know they can rely on my expertise.”
Brainteasers in Data Analyst Interviews
Interviews for analytical and technical positions often include brainteasers that aim to evaluate how you apply logic, critical thinking, and creativity under pressure. Here’s an example:
21. A car travels a distance of 60 miles at an average speed of 30 miles per hour. How fast does the car need to travel on the way back (taking the same road) in order to average 40 miles per hour over the course of the entire trip?
You need to build the following equation:
The total distance that needs to be traveled both ways is 120 miles. The average speed that we need to obtain is 40 miles; therefore, the car must travel for 3 hours in order to achieve that:
120 miles/40 miles per hour = 3 hours
The car has already traveled for two hours:
60 miles/30 miles per hour = 2 hours
So, on the way back it needs to travel only 1 hour. The distance is 60 miles. Hence the car needs to travel at 60 miles per hour.
Guesstimate in Data Analyst Interviews
Guesstimates can be critical in picking the right candidate for a data analyst job, as they assess your problem-solving abilities, confidence with numbers, and how you handle different scenarios.
22. What is the monthly profit of your favorite restaurant?
Pick a small family restaurant and not a chain of restaurants. This should make calculations much easier.
Then define the main parameters of the restaurant that we are talking about:
- Days of the week in which the restaurant is open
- Number of tables/seats
- Average number of visitors:
- during lunchtime;
- at dinner;
- Average expenditure:
- per client during lunch;
- per client during dinner.
The restaurant is open 6 days of the week (they are closed on Monday), which means that is open 25 times during lunch and dinner time per month. It is a small family restaurant with around 60 places. On average 30 customers visit the restaurant at lunch and 40 people come to have dinner. The typical lunch menu costs 10 euro, while dinner at this restaurant costs twice that amount – 20 euro. Therefore, they are able to achieve revenues of:
25 (days) * 30 (customers) * 10 (EUR) = 7,500 EUR (lunch)
25 (days) * 40 (customers) * 20 (EUR) = 20,000 EUR (dinner)
The restaurant is able to achieve 27,500 EUR of sales. Besides, the owner and his wife 4 people work there as well. Let’s say that the 3 waiters make 2,000 EUR each and the chef makes 3,000 EUR (including social security contributions). So the cost of personnel is 9,000 EUR. Usually, food and drinks cost around one-third of the overall amount of sales. Therefore the cost of goods sold amounts to 9,125 EUR. Utility and other expenses are another 10% of Sales, so we will have an additional cost of 2,750 EUR. The owners do not pay rent, because they own the place. After the calculations that we made, it results in a monthly profit of (before taxes) 6,625 EUR.
Real Examples of Data Analyst Interview Questions and Processes From Big Companies
Phone screens, onsite interviews, number of teams who ask the data analyst interview questions… All of these vary depending on the company you’re applying at. Here are 3 real processes for data science positions you get a sense of how a data analyst interview goes down.
Two detailed data analyst phone interviews – one with a recruiter, followed by another one with the hiring manager. There are also two onsite interviews. The first one is with about 4 people from the data analyst team. So, you can expect plenty of analytical, statistical (mostly A/B testing), and some SQL programming and stats principles questions. Most probably, you’ll be asked to analyze an assumed problem and identify key product management metrics. The second data analyst interview is with higher-level executives. Usually, questions are centered around the candidate’s background and professional experience.
LinkedIn’s interview process for hiring data analysts doesn’t differ much from other companies. That said, there are phone screen interviews (expect some SQL and Python skills questions there), as well as 4 or 5 onsite data analyst interviews. About half of them focus on more advanced analytics questions, while the rest aim to assess your coding skills, and statistical knowledge (e.g. Simpson’s paradox). Something specific for the LinkedIn data analyst interview questions – a lot of them are product-related and require a product mindset and quick thinking. You may also encounter questions related to data applications and recommenders they use in their product.
Google’s data analyst interview process is quite standard.
You’ve got one or several phone screen interviews, followed by onsite interviews. The first phone screen is usually centered around technical data analyst questions (some candidates share they were given an online SQL test, as well). By the way, Google has its own guide for the technical part of the interviewing process and you can check it out here. The onsite interviews are conducted by 4 to 6 people. All interviewers keep their notes confidential. Therefore, the possibility of bias in the data analyst interviewers’ feedback is down to a minimum. The next step is sending the written feedback to a hiring committee (something specific for Google). Finally, the committee makes a recommendation to Google executives for approval. Anything to keep in mind? The Google hiring process can take longer than expected. So, don’t be afraid to politely request a status update if a week has passed.
How to Make a Great Impression During the Data Analyst Interview?
Interviewing for a data analyst position may seem a bit stressful at first. So, just in case you still feel challenged in the confidence department, as a final takeaway, take a page out of our playbook. Here’s what we’re looking for when we’re hiring expert data analysts to develop the 365 courses:
- Be a good listener, so pay attention to every single word in the data analyst interview questions;
- Make sure your explanations are clear and reflect your thought process;
- Well, even if your answers aren’t perfect and you need some help from the interviewer, you can still make it work. Being open to receiving help means you can handle feedback and tells the interviewer you’ll probably be a solid team-player;
- Communication (both verbal and non-verbal) is key – exude a positive attitude, demonstrate professionalism and be confident in your abilities. Keep in mind your tone of voice and pacing, as well as your gestures. Your body language speaks volumes! That said, you can find more about the types of non-verbal communication and how to improve your body language in this Indeed article.
Data Analyst Interview Questions and Answers 2021: Overview
Last but not least, if you didn’t land the data analyst job, learn from your experience. Try making mock data analyst interviews with a friend or a colleague. Include the challenging data analyst interview questions you couldn’t answer before and find a solution together. That will make you feel more self-assured next time you go to a data analyst job interview. To quote Mark Meloon,
“Chase fewer jobs but do a better job on them and do a post-mortem afterward so you can learn.”
That said, when you’re consistent and manage to stay organized in your data analyst job search, good things happen.
Ready for more data analyst interview questions? Follow the link to our really detailed article Data Science Interview Questions And Answers. And, if you're looking to turn your interest in data science into a dedicated career, check out our course Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process.