Data analyst job interview: ace these common questions

Interested in becoming a data analyst but nervous about your job interview? You’ve come to the right place. We’re breaking down the most common questions you’ll be asked in an interview. Plus, the best way to answer them - directly from the pros.

For even more guidance looking for a data analyst job, apply for our Data Analytics Program for lifetime career services support and guidance to help start and grow your career. You’ll gain access to our experienced team of mentors and guides who work closely with you to ensure you’re ready to launch your data career. In fact, 89% of our data grads find a job within 180 days because of our personalized, and hands-on approach to education and career training.

How Do I Prepare for a Data Analyst Interview?

Woman interviewing for a data analyst job Preparing to interview for a data analyst position calls for some forethought. First, research the company that you are interviewing with and the team this position is within. Second, determine your relevant skills and experience, which you want to bring up during the interview. Have examples of past projects and achievements to cite in conversation. Next, practice typical interview questions and get comfortable with the answers you want to deliver in the interview. Then, go into the interview with a positive mindset; whether you get a job offer or not, approach this experience as a learning and growth opportunity.

Finally, it helps to do some research on the company itself for any previous candidate interview experience. Sites like Glassdoor provide crowd-sourced data points on the types of questions companies ask, the culture, and what the interview process is like.

Data Analyst Interview Questions and Answers

Applying for a data analyst job is both exciting and nerve-wracking. Before attending your data analyst interview, it’s important to prepare yourself. Make sure you have a solid understanding of what a data analyst does, including common tasks they complete, the software they use, and how to solve certain problems. Don’t forget to think back on your past experiences so you can recall moments that you can use as examples while answering questions. Read on to prepare yourself for your next interview, and land that dream job of yours:

Subjective Questions

In an interview, these questions are more likely to appear to help employers learn more about the candidate.

Why do you want to become a data analyst?

This question helps the interviewers understand your thought process behind choosing the role. When answering, make sure to explain the key reasons you want to be a data analyst and what key skills you have for the job.

Potential Answer:

"As a data analyst, it’s my job to help you make more informed decisions to help the company improve. I’m good with collecting data, communicating my findings to others, and I find market research interesting."

Have you worked in an industry similar to ours?

By answering this question, you’re demonstrating that you have industry-specific skills and experience. If you haven’t worked in this industry before, be honest and explain how you can apply your current skills to benefit the company.

Potential Answer:

“I’ve previously worked in the healthcare industry, but I think there are quite a few overlaps with the financial industry. One being data security. Both these industries have large amounts of highly sensitive information that has to be kept secure and confidential. Because of this, data is often restricted, so it takes more time to complete an analysis. Having dealt with this many times, I’ve learned how to be efficient when it comes to passing through the security and how to clearly state the reasons behind requiring certain data."

How do you handle pressure and stress?

The point of this question is to understand how well you work in stressful situations. Think of a time when you were given a difficult task or multiple tasks and excelled in the project.

Potential Answer:

“I find I work really well under pressure as I enjoy working in a challenging environment and thrive under quick deadlines. I often perform my best work when I have the pressure of a deadline coming up. For example, I once had 5 large projects due within the same two weeks. Although it was stressful, I created a detailed schedule that broke down the projects into smaller assignments and staggered the due dates. I ended up completing the projects on time with no added stress.”

Why should we hire you?

Think about what truly makes you the best fit for this question. Keep your answer short and confident, explaining exactly what you have to offer.

Potential Answer:

“I have previous experience working with projects that had similar problems to yours. I also have excellent communication skills and further technical knowledge that would be an asset to your company. The mix of technical and team skills I bring to the table makes me an ideal fit for this role.”

What soft skill(s) are you best at? Why?

Non-technical skills are important in any job to ensure that you’re working efficiently with others and can perform your job at a high level. Think about important strengths or skills for performing your role, outside of the typical data analyst skills. State 2-3 skills and briefly (and confidently) explain why you think you’re good at them.

Potential Answer:

“I believe my leadership skills help me take action easier and help other members of my team. It also helps me continuously want to learn and grow so I can be the best leader possible - whether that means developing further soft skills or technical skills. My ability to listen is also very helpful as I’m generally interested in my field and learning from others, and I want to ensure that I understand the project so I can complete it to the best of my ability.”

What do you think are the most important skills a data analyst needs to work efficiently with people who have different roles, knowledge, and duties?

As a data analyst, you’ll report findings to multiple stakeholders without a background in data interpretation. It’s important to have skills in interpreting your findings using non-technical language and showing that you’re capable of working with various people who don’t speak your “language”.

Potential Answer:

“I believe patience, understanding, and showing that you care are all very important when working with people of different educational backgrounds. I often work with stakeholders, and the most common challenge is trying to answer a question I don’t have the answer to yet, due to the limited data I have at the moment. When this situation arises, I use my available data to answer the question as closely as possible and then propose how I can find the information that we don’t currently have. This not only shows that I’m dedicated to the project but that I also respect their needs.”

How have you used statistics in your work?

Understanding basic statistics knowledge is important for data analysts. This question helps the interviewer see how much you know about statistics.

Potential Answer:

“I have used statistics before - mostly calculating the mean and standard variances, as well as significance testing. I’ve also determined the relationship between two variables in a data set while working with correlation coefficients.”

Do you have any questions?

Every interview will end with this question, but it’s important to have some questions prepared instead of just saying no. Try asking any of the following questions to show that you’re interested in the position and eager to work there:

  • Can you explain a typical day for a data analyst at your company?
  • What’s the work environment like in the office?
  • What’s the company culture like?
  • Are there any options to further my learning so I continually grow in my role?

While answering any questions yourself, make sure you sound natural and let your personality shine. You don’t want to sound like you memorized a speech. Try and have a conversation with your interviewer and be confident in what you can bring to the table. Remember to always tie back your experience or examples to the company and position you're interviewing with and for. Not only does it show an understanding of the scope of the role, but it shows that you did your research ahead of time which can go a long way.

Objective Questions

In an interview, these questions are more likely to appear to help employers learn more about a candidate’s skills in data analytics.

What are the key components of a data analyst job?

This question tests your knowledge about the required skills for a data analyst.

Potential Answer:

"I have a solid understanding of various programming languages such as Python, and databases like SQL and Db2, and have extensive knowledge of reporting packages.

  • Understand Big Data and be able to analyze, organize, collect, and disseminate it efficiently.
  • Develop substantial technical knowledge in fields such as database design, data mining, and segmentation techniques.
  • Understand how to analyze massive datasets. A few examples are SAS, Excel, SPSS."

What is the difference between data mining and data profiling?

Data mining is the process of discovering patterns, trends, and insights within a large dataset. It aims to identify relationships, anomalies, or patterns in the data.

Data profiling is the process of examining and analyzing datasets to evaluate their structure, content, quality, and integrity. However, it cannot identify incorrect or inaccurate data values.

What is data wrangling?

Data wrangling is the process of transforming and cleaning raw data into a structured format suitable for analysis. It involves cleaning data by correcting data types, standardizing data formats, and dealing with duplicates. The final goal is to restructure the data from multiple sources to a useful format for data analysis.

What are the common problems that data analysts face during data analysis?

  • Inaccurate, incomplete, or inconsistent data
  • Multiple data sources in different formats
  • Ensuring data privacy and compliance with regulations
  • Unintentional biases or subjective interpretations

Tell us about your most difficult project as a data analyst. How did you solve it?

Explain to employers the type of projects you’ve done in the past, and how you approach issues and solve problems. Talk about the project, the solution, and the result. Avoid blaming others or explaining why the project was difficult.

Potential Answer:

“My most difficult project was finding out the percentage of pollution in the United States. I had to figure out which states are the most polluted, and I also compared the pollution levels in the last 10 years and predicted what it would be in the upcoming 10 years. I had some data, but I did further research on the most polluted states to help finalize my future predictions. After my analysis, I’m confident in the results.”

Which data analyst software are you comfortable using?

As a data analyst, you are expected to know the basic software needed to perform your job. Make sure to explain whether you know the software stated in the job description.

Potential Answer:

Microsoft Excel/Google Sheets/Tableau:

  • For data manipulation, data analysis, and visualization. These are commonly used to sort, filter, and perform basic calculations.

MS SQL Server, MySQL - SQL is crucial for querying and managing relational databases.

Python/R Programming

  • Python and R are versatile programming languages widely used in data analysis.

Apache Hadoop and Spark

  • Open-source software frameworks are used to distribute storage and process large datasets. Handling big data and performing complex analysis on vast amounts of information.


  • Version control tools like Git are essential for documentation and tracking changes in collaborative projects.

Visual Studio Code (VSCode)

  • Source code editor for software development and data-related fields.

MS Powerpoint

  • For presenting results

What is “data cleansing”? What’s the best way to practice?

One of the most common questions in a data analyst job interview - so make sure to have an answer! Simply explain what data cleaning is and how to do it.

Potential Answer:

"Data cleansing involves detecting errors and inconsistencies and removing them from the data to improve its quality. Common ways to clean data are:

  • Compartmentalizing data according to their attributes
  • Breaking large chunks of data into smaller datasets to clean them
  • Analyzing the statistics of each data column
  • Creating a set of scripts that solve common cleaning tasks
  • Keeping the information organized to facilitate easy addition or removal from the datasets, if required.”

Why is Exploratory Data Analysis (EDA) important?

  • It helps analysts understand the dataset.
  • It helps identify how to manipulate data sources by identifying patterns, relationships, and trends within the data.
  • It lets you see what data can reveal beyond hypothesis testing or formal modeling.

Have you previously used quantitative and qualitative data within the same project? Tell us about it.

Using both quantitative and qualitative data within a project is important to gain a full understanding of the project. When answering this question, talk about the project that required the most creative thinking.

Potential Answer:

“I’ve had a few projects where I had access to qualitative survey data, but I realized that I can enhance the validity of my recommendations by implementing data from external survey sources as well. When I combined the two types of data together in a product development project, it yielded great results.”

What sampling techniques do data analysts use?

  • Simple random sampling
  • Stratified sampling
  • Systematic sampling
  • Cluster sampling
  • Snowball sampling
  • Purposive sampling

What are the two main types of hypothesis testing?

Null Hypothesis (H0)

The null hypothesis is a statement that suggests there is no significant difference or relationship between the variables being tested. It's usually denoted as "H0".

Alternative Hypothesis (H1 or Ha)

It suggests that there is a significant difference, effect, or relationship between the variables being tested. It's denoted as "H1".

What is Time Series analysis?

Time Series Analysis is a statistical technique used to analyze patterns, behaviours, and trends among data collected over regular time intervals. It involves studying the sequence of data points measured across equal time intervals.

What is the difference between a Type 1 and Type II error?

Type I error occurs when a null hypothesis (H0) is incorrectly rejected. It is considered a false positive.

Example: The test results indicate you have a fever, but you don’t.

Type II errors occur when a null hypothesis (H0) is not rejected, even when it is false.

Example: The test results indicate you don’t have a fever, but you do.

What should you do if data is missing or suspected?

This is another question that the interviewer asks to gain an understanding of how you solve problems. These are some of the most common ways to solve data problems:

Listwise Deletion

Eliminate entire rows with missing values. It's simple but can result in losing a significant amount of data.

Average Imputation

Replace missing values with the mean, median, or mode of the respective column. This method is simple but may distort the original distribution of the data.

Regression substitution

A method used to impute or fill in missing values in a dataset using regression analysis.

Potential Answer:

"If data is missing or suspected, I would try:

  • Using the deletion method, single imputation method, and other model-based methods to detect missing data.
  • Preparing a report containing all the information about the missing or suspected data
  • Replacing the invalid data with a proper validation code, and;
  • Analyzing the suspicious data to assess its validity"

What’s your experience in giving presentations to various audiences?

Communication is a very important skill as a data analyst, and that includes being able to give strong presentations. Employers are looking for candidates who have great analytical skills and the confidence to present their findings in an eloquent and easy-to-understand way to upper-level management and non-technical co-workers.

When answering this question, make sure to mention the following:

  • Size of the audience you presented to
  • Who was in the audience (ex. executives)
  • The general knowledge the audience had
  • If the presentation was in person or remote.

Potential Answer:

"I’ve presented to various audiences. Some were smaller, with many upper-level management and executives present, while others were larger and included coworkers and clients that had different knowledge backgrounds. The largest presentation was around 50 people. All of these presentations were done in person, except for a one-on-one Zoom call with the CEO.”

How do you find outliers in a dataset?

Outlier detection is the process of identifying observations in a dataset that deviate from the rest of the data points.

It's crucial to understand the context of the data and the reason behind outliers before removing them. Outliers might represent true extreme values or errors in data collection. It is an important distinction to ensure accurate and meaningful analysis.

What are the ethical considerations of data analysis?

Data analysis involves handling sensitive information and making decisions that impact individuals, so it’s important to be aware of key ethical considerations, which include:

  • Data Privacy and Confidentiality: Ensure sensitive personal information is handled and stored securely by following data protection laws (e.g., GDPR, HIPAA).
  • Bias: Unintentional biases can lead to unfair treatment or discrimination.
  • Informed Consent and Transparency: Obtain informed consent from respondents and be transparent about how their data will be used. Individuals should be aware of the purpose of data collection and its potential impact.
  • Data Ownership and Responsibility: Respect the rights to data ownership and handle data responsibly. Establish clear policies on data ownership, usage, and sharing.
  • Data Quality and Accuracy: Ensure data used in analysis is accurate, reliable, and of high quality. Inaccurate or misleading data can lead to incorrect conclusions and decisions.
  • Data Retention and Disposal: Store data only for necessary periods and dispose of it securely when no longer needed.
  • Security and Protection: Protect data from breaches, unauthorized access, and cyber threats. Implement robust security measures to safeguard data integrity.

What’s your favourite software to use?

Just be honest and explain what program you like best and why. If you enjoy a software that the company currently uses, it would be helpful to state that one. If you find the role requires specific software skills that you don't have, do a few tutorials ahead of hand and demonstrate that you're a quick learner and willing to pick up on these tools quickly.

Potential Answer:

“I personally love Microsoft Excel because they’re available at almost every company, and I’m very comfortable using them. Since they’re so accessible, there is a lot of training on them, so I can continually improve my skills to achieve great results. I also love working with visualization software like Tableau and Power BI, as well as other tools like Jupyter Notebook, Seaborn, and more to help round out my skill set.”

What step of data analysis do you enjoy the most? Why?

Although you may not love all the steps of data analysis, use this question to show your strengths instead of talking about what you don’t like.

Potential Answer:

“If I had to choose one favourite step, it would be analyzing the data. I enjoy finding evidence to support or refute various hypotheses, and I think it’s really interesting to find unexpected learnings from the data. You can learn so much from data, and it always helps with my analysis of future projects.”

What scripting languages have you used? Which one do you like best?

Although it’s helpful to know more than one language, as many companies use a variety, it’s more important to demonstrate your enthusiasm for learning new scripting languages and point out your fluency in the ones you have a solid understanding of. When answering this question, keep in mind what languages that company uses. If you’ve used it, state why you like it. If you haven’t, explain which language is your favourite but also mention how you’re open to learning others.

Potential Answer:

“My favourite scripting language is SQL because I've worked with it the most so far, so I’m quite confident using it. But, I’ve also been learning Python. I’ve found my knowledge in SQL helps me better understand Python, so I’m able to learn it faster.”

What are the key requirements for becoming a data analyst?

To become a data analyst, you should have a combination of the following skills:

  • Structured Query Language (SQL)
  • Microsoft Excel
  • Critical Thinking
  • Python-Statistical Programming
  • Data Visualization(Tableau or other visualization tools)
  • Presentation Skills
  • Mathematics and Statistics

Generally, the following responsibilities fall within the data analyst role, so you should be comfortable with:

  • Utilize data analysis skills to become experts in the performance of specific businesses and departments.
  • Tend to be specific to a single team or department, like Sales, Marketing, or Customer Experience.
  • Implement basic scripts and pipeline code, but typically are not expected to develop software.

Start Your Data Analytics Career with Lighthouse Labs

If you don’t have the answers to most of these answers but want to be a data analyst, we’d recommend getting your feet wet with our Intro to Data Analytics course.

Or, if you're ready to jump right in, join our Data Analytics Program.