Data Analyst Interview Questions

How to Answer the Top Data Analyst Interview Questions?

Author: Sreelakshmi Sreejith
Sreelakshmi Sreejith
Sreelakshmi Sreejith
As an Economics undergraduate at the University of Birmingham, I'm fueled by a passion for decoding intricate economic challenges using data-driven insights. Proficient in unraveling complex puzzles through SQL, STATA, Tableau, and Power BI, I delve into datasets with precision. Beyond financial and economic analyses, my leadership role as Vice President at The Creative Pod allows me to refine my skills and craft impactful strategies, shaping a pathway to success.
Reviewed By: Matthew Retzloff
Matthew Retzloff
Matthew Retzloff
Investment Banking | Corporate Development

Matthew started his finance career working as an investment banking analyst for Falcon Capital Partners, a healthcare IT boutique, before moving on to work for Raymond James Financial, Inc in their specialty finance coverage group in Atlanta. Matthew then started in a role in corporate development at Babcock & Wilcox before moving to a corporate development associate role with Caesars Entertainment Corporation where he currently is. Matthew provides support to Caesars' M&A processes including evaluating inbound teasers/CIMs to identify possible acquisition targets, due diligence, constructing financial models, corporate valuation, and interacting with potential acquisition targets.

Matthew has a Bachelor of Science in Accounting and Business Administration and a Bachelor of Arts in German from University of North Carolina.

Last Updated:February 18, 2024

On successfully passing the initial CV screening, candidates proceed to the interview stage. However, companies sometimes send out an initial online assessment before the interview stage. Following this assessment, they conduct the main interview.

Companies may conduct different kinds of interviews, depending on their time and convenience. This includes:

  1. Phone screenings: Firms conduct telephone interviews with the main objective of filtering out unqualified candidates. It allows the recruiter to see if the person is interested in the job and ask questions regarding the candidate's work history and expertise. Only by impressing the recruiter at this stage can a candidate pass on to the next level.
  2. Technical assessments: After successfully clearing the phone interview, the candidate will be presented with assessments designed to test your technical and problem-solving abilities. The business acumen and ability to derive valuable perspectives will also be tested here.
  3. In-person interview: This is the most important stage of a data analyst’s interview. This stage could include various components such as technical interviews, behavioral interviews, and a panel discussion.
  4. Virtual interview: Preparation for a virtual interview is similar to that of an in-person interview. A candidate's additional responsibility is to guarantee a seamless online experience. This includes having a stable internet action, ensuring proper functionality of the microphone and camera, and arranging appropriate background and lighting.

Interview Tips

  1. Dress well! : Understand the company’s culture and dressing sense from social media posts. When in doubt, lean towards the formal side. This could include suits, dresses, and pantsuits. Ensure your attire is neat and well-groomed.
  2. Interviewers read into your body language: Maintain good posture and eye contact. Smile, and don’t be tense. Actively listen and show that you are occasionally nodding. Try mirroring the interviewer's energy and pace. This builds good rapport. 
  3. Stumped by a Question? Stay Calm! Break down the question into smaller parts and answer each part to the best of your ability. If you have relevant information or experience, even if not directly related, talk about it.
  4. Turning Lack of Knowledge into Strength: If you genuinely do not know the answer to a question, admit it. However, pivot it to your strengths. Show them that you’re willing to learn. If, after the interview, you think of a better answer, follow up. 
  5. Ask Questions!: Post the interview, you will be asked if you have any questions for the interviewer. This opportunity should be used to gain insights into the company.

Data Analyst Interview Reviews

To gain a solid grasp of data analyst interviews, let's start by learning from the experiences of other candidates.

Here are some reviews from data analyst interviews:

1. Royal Bank of Canada (RBC)

An intern who interviewed at the Royal Bank of Canada (RBC) emphasized the value of using the STAR method while answering situational and behavioral questions. Furthermore, they highlighted the importance of knowing the resume thoroughly.

2. Xerox Corporation

An analyst at Xerox Corporation reported a “Very Positive” overall experience and an “Average” difficulty level. They had to go through multiple rounds of interviews.

3. Bloomberg LP

A market data analyst at Bloomberg LP experienced the regular interview process. The only difference was in their in-person interview. This stage had additional parts, such as a fun group debate, coding multiple choice test, and a workflow case interview with two employees.

This analyst described the workflow case interview to be challenging. They also shared that the interviewers, when asking technical or case study questions, look for value-added solutions that reduce time in their process.

4. Google

A data analyst at Google had an interview that consisted of discussions, problems, and scenarios that required pro-activity. They emphasized the importance of building a good rapport with the interviewer, which helped set a positive tone for the rest of the interview.

5. Moody’s Corporation

A 1st Year analyst at Moody’s Corporation described their interview as fair. A shortcoming of the process was that the interviewers took a while to respond and were hard to reach.

All candidates reported a “Positive” overall experience and an “Average” difficulty level. 

Preparations For The Interview

Heading into an interview can be daunting, especially if you’re a fresher. The best way to approach this is to be well-prepared.

Below are some valuable things that will ensure you are best equipped.

  1. Review Your Resume: Thoroughly review your resume and know your work history down to the finest details. Ensure it is updated and aligns with the job description of the role you are interviewing for. 
  2. Build a Portfolio: As a data analyst, it is essential to have a portfolio of your projects. Your best work must be in your portfolio, and you should be able to describe them confidently.
  3. Practice Technical Skills: Excellence is not created overnight; rather, it is through hard work. Keep practicing your coding skills (those relevant to data analysis). Furthermore, know your fundamentals. Keep a sheet of paper that lists all the core concepts you need to know and read through them regularly.
  4. Mock Interviews: Mock interviews are one of the best ways to face a real interview. You can use online resources that give you a real feel of the interview and also give you feedback on your answers. 
  5. Research the Company: Do comprehensive research on the company you are interviewing for. Read their blogs, articles, and posts. Be aware of what is currently going on at their workplace. Know their mission and vision, and understand how it aligns with your goals and experience. 
  6. Stay Industry-Informed: Stay up to date with your industry trends and updates. Data analysis is an ever-growing field with constant AI, machine learning, and software updates. Subscribe to technological newsletters to stay informed! 

Data Analyst Interview: Technical Questions

During this evaluation phase, your technical aptitude in data analysis will be assessed. The questions will delve into fundamental data analysis concepts, encompassing data manipulation, statistical analysis, and data modeling.

Moreover, your proficiency in essential programming languages, namely SQL and Python, will undergo rigorous scrutiny.

These languages are indispensable tools within the data analyst's arsenal, facilitating data extraction, transformation, and analysis. Your ability to manipulate data efficiently and derive meaningful insights will be tested.

Furthermore, your familiarity with industry-standard data visualization software, specifically focusing on widely used platforms like Tableau and Power BI, will be tested.

Proficiency in these tools is instrumental in effectively conveying data-derived insights to stakeholders, aiding in informed decision-making processes.

You will be expected to showcase your prowess in crafting engaging and informative data visualizations that empower organizations to make data-driven choices.

Core Concepts

When it comes to questions regarding core concepts, interviewers typically probe candidates on their understanding of:

  • Key project workflows
  • Data preprocessing skills
  • Conceptual distinctions between related terms
  • The importance of Exploratory Data Analysis (EDA)
  • Ethical considerations in data handling. 

Practical examples from past experience are often valuable for demonstrating proficiency in these areas during interviews. Let us see some of the sample questions and answers:

Data Analyst Technical Questions - Core Concepts

Statistical concepts

When asked questions related to statistics and data analysis, interviewees can expect a range of inquiries to assess their fundamental knowledge and problem-solving abilities.

These questions evaluate the candidate's grasp of essential statistical principles and their capacity to apply these concepts in real-world data analysis scenarios. 

Data Analyst Technical Questions - Statistical Concepts

Excel Concepts

When asked questions related to Excel functions and concepts, interviewees can expect a range of inquiries designed to gauge their proficiency in spreadsheet software and data manipulation.

Candidates should be prepared to provide concise explanations and practical examples of these concepts to showcase their spreadsheet expertise and analytical abilities.

Here are the different kinds of questions you could get in an interview about Excel: 

Data Analyst Technical Questions - Excel Concepts

SQL Concepts

Interviewees can expect questions related to core concepts in SQL, such as query components, data types, SQL injection prevention, and the use of stored procedures, during interviews for data analysis or database-related roles.

These questions assess candidates' fundamental knowledge of SQL and their ability to extract, transform, and manage data effectively.

Here are the most common interview questions and concepts related to SQL:

Data Analyst Technical Questions - SQL Concepts

Python Concepts

Interviewees can expect questions related to core concepts in data analytics using Python to assess their knowledge and practical skills in data manipulation, cleaning, analysis, and visualization.

Candidates should be prepared to demonstrate their theoretical understanding and ability to apply these concepts to real-world scenarios.

Provided below is a list of the most commonly encountered questions relating to Python and some sample answers:

Data Analyst Technical Questions - Python Concepts

Data Visualization Concepts

Depicting data in an informative way that reaches all kinds of audiences is a fundamental aspect of being a data analyst.

An interviewer aims to understand whether their candidate knows the best ways to interpret and visualize the insights they obtained from their data analysis process. It also tests their knowledge of the two most prominent data visualization tools - Power BI and Tableau.

Below are the most commonly encountered questions and their exemplary answers - 

Data Analyst Technical Questions - Data Visualization Concepts

Data Analyst Interview: Behavioral Questions

When answering behavioral questions, using the STAR (Situation, Task, Action, Result) method is best. Here’s a description of this method -

  • S - Situation - Describe a situation you were in that relates to the question asked. Add in any specific but relevant details. The recruiter should have a clear understanding of the situation you were in.
  • T - Task - After explaining your situation, you must discuss your responsibilities. Explain what you were expecting and your role to the interviewer. 
  • A - Action - Following explaining your role, talk about what actions you took or implemented. Talk about your contributions, the tools you used, and the skills it built in you. Use strong action verbs to impress the interviewer. 
  • R - Result - Here, you must talk about any successful outcome resulting from your action. Quantify your results. For example - ‘The action I took increased profits by 30%.’ Talk about any positive or valuable lesson you learned. 

Let's understand how to use this strategy by looking at sample answers to the most common behavioral interview questions for a data analyst. 

Data Analyst - Behavioral Questions


This article has explored the various aspects of data analyst interviews, including different types, essential interview tips, and real-world interview experiences from fellow data analysts.

Additionally, we have comprehensively examined interview questions, covering core concepts, statistical knowledge, Excel proficiency, Python skills, and data visualization expertise.

Moreover, with technical prowess, the significance of behavioral questions in assessing interpersonal and problem-solving skills has been highlighted.

As you prepare for your next data analyst interview, remember that your greatest asset is a blend of knowledge, preparation, practice, and a composed demeanor.

Best of luck in your upcoming interviews as you advance in the dynamic field of data analysis!  

Researched & Authored by Sreelakshmi Sreejith | LinkedIn

Reviewed and edited by Parul Gupta | LinkedIn

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