It's More Than Just Numbers
A data analyst interview is a blend of technical skill assessment and a test of your business acumen. The interviewer wants to know: can you work with data, can you derive insights from it, and can you communicate those insights effectively to non-technical people? Being able to write a perfect SQL query is only half the battle. You also need to demonstrate your problem-solving skills and your ability to think like a business stakeholder. This guide breaks down the most common types of questions you'll face, from technical challenges to behavioral scenarios.
Technical Questions
1. SQL: "What is the difference between `JOIN` and `UNION`?"
Why they ask it: This is a fundamental SQL concept. They want to ensure you understand how to combine data from different tables and datasets.
How to answer: "A `JOIN` is used to combine rows from two or more tables based on a related column between them. For example, joining a `customers` table and an `orders` table on `customer_id` to see which customers placed which orders. A `UNION` is used to combine the result sets of two or more `SELECT` statements into a single result set. It appends the rows from one query to another and removes duplicate rows, whereas `UNION ALL` includes all rows."
2. SQL: "Given these two tables (Orders, Customers), write a query to find the top 5 customers with the highest total order value."
Why they ask it: This is a practical test of your ability to write a real-world query. It tests your knowledge of joins, aggregate functions (`SUM`), `GROUP BY`, and ordering (`ORDER BY`).
How to answer: Be prepared to write the code on a whiteboard or in a shared editor.
SELECT c.customer_name, SUM(o.order_value) AS total_spent FROM Customers c JOIN Orders o ON c.customer_id = o.customer_id GROUP BY c.customer_name ORDER BY total_spent DESC LIMIT 5;3. Statistics: "What is the difference between Type I and Type II errors?"
Why they ask it: This tests your foundational knowledge of statistical hypothesis testing, which is crucial for making data-driven decisions.
How to answer: "A **Type I error**, or a false positive, is when you incorrectly reject a true null hypothesis. For example, concluding that a new website design increases sales when it actually doesn't. A **Type II error**, or a false negative, is when you incorrectly fail to reject a false null hypothesis. For example, concluding that the new design has no effect on sales when it actually does."
4. Python/R: "How would you handle missing values in a dataset?"
Why they ask it: Data is rarely clean. This question assesses your practical data cleaning skills.
How to answer: "My approach would depend on the context and the nature of the missing data. First, I'd analyze the extent and pattern of the missingness. If it's a small percentage, I might consider **imputation**, such as replacing the missing values with the mean, median, or mode. For time-series data, I might use forward or backward fill. If a column has a very high percentage of missing values, it might be better to **drop the column** entirely. Alternatively, if rows have critical data missing, I might **drop those rows**. The key is to choose a method that introduces the least amount of bias."
Case Study & Behavioral Questions
5. "Our user engagement dropped by 10% last week. How would you investigate this?"
Why they ask it: This is the ultimate data analyst case study. It tests your structured thinking and problem-solving process.
How to answer: Use a structured approach. "First, I'd clarify the question. What do we mean by 'user engagement'? Is it daily active users, time spent on site, or another metric? Is this drop sudden or part of a trend? Then, I'd form a few hypotheses. Was there a technical issue, like a bug in a new release? Was there a change in our marketing channels? Is it specific to a certain user segment (e.g., new users, mobile users) or a particular geography? I would then dive into the data, using SQL and Python, to test these hypotheses one by one to isolate the root cause."
6. "Tell me about a project where you used data to make a recommendation that had a significant impact."
Why they ask it: They want to see that you can not only analyze data but also translate it into actionable business insights. This is a perfect time to use the STAR method.
How to answer: "**(S)ituation:** In my last role, the marketing team was spending a lot on a campaign with low conversion rates. **(T)ask:** My task was to analyze the campaign's performance and recommend whether to continue, modify, or stop it. **(A)ction:** I analyzed the customer journey data and discovered that while the campaign had a high click-through rate, users were dropping off on the landing page. I then compared the demographics of the ad audience to our core customer base and found a significant mismatch. **(R)esult:** I recommended that we pause the current campaign and relaunch it with a retargeted audience that more closely matched our ideal customer profile. After implementing this change, the conversion rate increased by 150%, and the cost per acquisition dropped by 60%."
7. "How do you ensure the quality and accuracy of your data?"
Why they ask it: "Garbage in, garbage out." They need to know you are a responsible and detail-oriented analyst.
How to answer: "Data quality is a priority. I start with exploratory data analysis to check for anomalies, outliers, and inconsistencies. I write data validation scripts to check for things like correct data types and ranges. I also make sure to understand the data's source and any transformations it has undergone. When possible, I cross-reference key metrics with other data sources to ensure consistency."
Need more practice? Our AI Mock Interview tool can generate a personalized set of questions based on your resume and a target job description.
Conclusion: Prepare, Practice, and Explain
Success in a data analyst interview comes from a combination of solid technical skills and the ability to communicate your thought process clearly. Practice writing queries, review your statistics, and, most importantly, prepare your stories using the STAR method. Show them you're not just a data cruncher, but a data-driven problem solver.