Skip to main content

How to prepare for a Data Analyst interview

If you are hunting for your first data analyst job, or looking to move up in your career, use this guide to help prepare for your interview and land your dream job.
Jun 2022  · 8 min read

On average, there are up to 100,000 job openings for data analysts worldwide with demand from top industries including Finance, Healthcare, and Entertainment. If you are hunting for your first data analyst job, or looking to move up in your career, use this guide to help prepare for your interview and land your dream job. 

Typical Data Analyst interview process

The data analyst interview process typically involves the following steps: 

    1. HR Interview: Your first step will involve a screening call with a recruiter to get a sense of your experience, interest, and salary expectations, as well as provide you with details about the position. 
    2. Hiring Manager Interview: The next call is normally with the hiring manager. They might ask more about your direct experience, as well as why you are interested in the position. 
    3. Technical Screen: This part is specific to data analyst roles. The technical interview can involve SQL and Python questions or a take home test. 
  • On-site interview: The final step tends to focus on your business acumen.

Once you have passed through these core parts of the interview process, you may have to wait some time for an offer. You may get a chance for feedback, however it’s not unusual to be ghosted if you haven’t heard back for a couple of weeks. 

These steps may seem overwhelming and unfamiliar. Technical screen? Hiring manager interview? Here are some typical questions asked of you in both general and technical interviews, to give you a better idea of what is expected and how to prepare. 

Data Analyst interview questions and how to approach them 

General data analyst interview questions:

What makes you the best candidate for the job? 

Although this can be a broad question, remember the interviewer wants to hear about you as a data analyst. So consider your journey with data analysis, what got you interested in the first place, your previous experience, and why are you applying for this role in particular. 

Tell me how you coped with a challenging data analysis project? 

They are asking for how you overcome challenges, and giving you a chance to highlight your strengths in action. Make sure to include strengths and weaknesses. Be honest about what went wrong or what you found difficult, and try to highlight any skills listed in the job requirements of this role. 

What type of data have you worked with? 

This question asks you to be as specific as possible. Focus on the size and type of data you have worked with, whether from previous work experience or your own projects and programs. Many hiring managers will be looking to see if you can handle large, complex data. 

Data analysis process questions:

What is data cleaning, and how do you do it? 

Data cleaning takes up a large part of your work hours as a data analyst. Here is a chance to show the interviewer how you handle the process, including missing data, duplicates, outliers and more. Be sure to explain why it is important, and how you have dealt with it in past projects. 

How do you communicate technical concepts to a non-technical audience?

Much of data analysis involves ordering your findings into a narrative, and clearly explaining it to both technical and non-technical audiences. This is where your soft skills come in: communication and storytelling. Give examples of how you’ve drawn insights from data and communicated those to audiences. These might include presentations to shareholders or written communication within your portfolio. 

How would you go about measuring the performance of our company?

When an interviewer offers up a question about the company, this is an opportunity to show your research into their work and how you align with them. Consider how your analysis skills can bring insights specific to this company in particular, with their problems and goals in mind. 

Technical skill questions:

What data analytics software are you familiar with?

What have you used before and what certifications do you have? How long have you been working with these kinds of software? This question helps the interviewer assess what level of experience you have, and how much training you might need for the role in question. 

Prepare by including any software listed in the job description that you have worked with, mentioning software solutions and how you have used them for different stages across the data analysis process. Be sure to include relevant terminology to keep on track. 

Software might include R, Python, Tableau and Microsoft Excel. Be sure to try some extra training in programs if you’re uncertain of these. 

What is your statistical knowledge? 

This question is usually asking if you have a basic understanding of statistics, and how you have used them in your previous data analysis work. 

If you are entry level and not familiar with statistical methods, make sure to research the following concepts: 

  • Standard deviation
  • Variance
  • Regression
  • Sample size
  • Descriptive and inferential statistics
  • Mean

If you do have some knowledge, be specific about how statistical analysis ties into business goals. List the types of statistical calculations you’ve used in the past and what business insights those calculations yielded. 

Can you define this term? 

This asks you to understand the terminology used in analytics. So make yourself familiar with the following: 

  • Normal distribution
  • Data wrangling
  • KNN imputation method
  • Clustering
  • Outlier
  • N-grams
  • Statistical model

Tips to prepare for your Data Analyst interview 

Here are DataCamp’s top tips to help you prepare for these questions, and your data analyst interviews: 

  • Research the business: Find out what the company’s problems or potential problems are. For example, what might be their current data problems? Who are their target audiences? With this research, plan how you might solve these problems using your experience. 
  • Research the interview format: At the beginning of the process, take the opportunity to ask the recruiter for direction. Seek out interview experiences and guides, using sources like Blinds or discussion posts. 
  • Identify your top skills: Through the interview process, some meetings will be focused on certain attributes more than others. For instance, in a technical interview you need to exhibit your experience with database language such as SQL. Think of your skills as combined and separate. Be prepared to discuss technical skills, analytics and visualization, as well as business acumen and soft skills. 
  • Study and  practice interview questions: Make use of programs such as Datacamp to practice technical skills, or build up your project experience, as well as business and analytics case studies. 

A few key things to remember before, during and after the interview

Before: Research and practice as much as you can, to align yourself, skills and experience with the business and role they are offering. Take into consideration whether the interview will be in person, ensuring you make technical checks with your video and sound if the meeting is taking place online. 

During: Stay alert to the questions being asked of you. Interviewers may ask general questions such as “tell me about yourself”, but remember all questions lead back to data analysis. 

After: Follow up your interviews with thank you emails to recruiters and hiring managers. Use the opportunity to connect with them post-interview to ask for feedback or any questions you didn’t get a chance to ask during the meeting. 

Exploratory Data Analysis in R

4 hours
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
See DetailsRight Arrow
Start Course

Exploratory Data Analysis in SQL

4 hours
Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

Exploratory Data Analysis in Python

4 hours
Learn how to explore, visualize, and extract insights from data.
See MoreRight Arrow
← Back to Blogs