A Data Analyst makes sense of complex data and code for businesses. Their technical and logical expertise helps companies create effective solutions to their problems and make informed decisions. The right Data Analyst for your business will be able to conduct thorough analyses of your business’s most important data, organize large data into meaningful results, and communicate their findings to your team.
A Qualified Data Analyst typically has a bachelor’s degree in computer science. They will be fluent in programming languages like Python, R., SQL and Java. Look for a creative problem solver with strong logical reasoning and communication abilities. Focus on candidates who appear to be resourceful and know how to maximize the top tools of their trade including Google Analytics, Tableau software and Microsoft Excel.
General interview questions (such as “Can you tell me about yourself?” and “Why are you looking for another job?”) are a great way to get to know your candidate’s personal history, interests, and goals. However, be sure to add inquiries specific to the role they’re interviewing for, so you can gain valuable insights into their likelihood of success in that position.
Below are Data Analyst interview questions to help you get started:
1. What made you decide to apply to our company?
What you want to hear: The right candidate will be able to easily explain why your business and industry appeal to them. They should offer clear reasons why your company is a good fit for them, ideally bringing up some of your core values and referencing your mission statement or company culture.
Red flag: A candidate who doesn’t seem to know anything about your company, its values or history is demonstrating a lack of preparation that may present itself on the job as well as the interview. While every job hunter applies to multiple positions, the candidate should make an effort to research each one and be able to explain why they want to work there.
2. What do you consider to be your core responsibilities as a Data Analyst?
What you want to hear: Data Analysts perform many functions depending on the nature of their exact position and the company they work for. Answers will vary based on each candidate’s work background and the job they’re applying for. Look for answers that showcase a Data Analyst’s ethical practices and a range of skills including cleaning data files, organizing and interpreting data to provide meaningful solutions, recognizing data patterns to provide insight, performing data maintenance, and quickly resolving code problems.
Red flag: Regardless of the position, every Data Analyst should be able to articulate what they do and why it matters. Candidates who do not truly understand the scope of their role may struggle to stay on task in future positions, resulting in underperformance or nonperformance.
3. What made you want to become a Data Analyst?
What you want to hear: Listen for a candidate who is passionate about data analysis and works toward a bigger purpose. Employees who simply show up to get the job done and clock-out never ultimately contribute to a company’s long-term growth. An ideal Data Analyst candidate will provide specific reasons for their career choice, and they’ll be able to explain why their skill set is well-suited for the role.
Red flag: Someone who views the data analysis role with a simple, linear mindset is revealing limited potential for creative problem-solving and proactivity. Candidates should know why they do what they do and, even more importantly, why they love to do it. Anyone who seems passively engaged in their field will most likely struggle to stay committed at work.
4. Can you break down your data cleaning process in a few steps?
What you want to hear: This question allows your candidate to demonstrate their professional experience, personal work ethic, and knowledge of your business. Listen for relevant contributions such as increasing productivity, maintaining schedules, operating within budget, and maintaining high rates of staff retention. The most valuable answers will weave in details the candidate may have learned about your specific company’s operations and goals.
5. What is your favorite type of analytics, and what is your least?
What you want to hear: Different types of analytics serve unique functions, and the candidate should be able to express which one is their favorite and least favorite with relevance to your business. Descriptive analytics, for example, provides insight into how things occur, while predictive analytics are used to predict likely outcomes.
Red flag: If a candidate can’t differentiate the types of analytics, or identify a favorite or least favorite, you should be highly skeptical of their skill set and experience. Data analysts may be more versed in a particular type of analytics than others, but they should still be able to differentiate them and justify their preferences.
6. Explain “clustering” and tell me the different types of clustering methods.
What you want to hear: A qualified candidate will explain that “clustering” is a task of grouping data into sets so that each set contains similar data when compared to other sets with differing data. The task is used for a range purposes including information retrieval, computer graphics, data compression, and pattern recognition. The different types of clustering methods include hierarchical clustering, fuzzy clustering, model-based clustering, partitioning methods, and density-based clustering.
Red flag: If a candidate is unable to discuss clustering, they may lack the experience needed to succeed in your role. Continue probing by asking additional questions about specific concepts or methodologies of data analysis to further evaluate the candidate’s qualifications.
Every interview question can help get you closer to the right fit for your Data Analyst position.
Be sure to keep an eye out for candidates who:
- Are well-versed in best practices and ethical conduct
- Demonstrate a knowledge of their profession’s reach and impact
- Are strong communicators and can express complex ideas in simple terms
What is a Pivot Table used for? Explain the different sections of a Pivot Table.
What does a Normal Distribution measure?
Explain Alternative Hypothesis and Null Hypothesis.
What does the ACID property in a database mean? Explain each term.
Identify the different types of Joins and explain how each one is used.
Describe a Dual Axis and share an example of how it can be used.
Explain the difference between a heat map and a tree map.
What are five advantages of Normalization?
Describe the basic syntax of writing code in SAS.
Explain the difference between variance and covariance, and describe what each one is used for.