Top Data Analyst Interview Questions (2025 Guide)

These are some of the most common data analyst interview questions.

This guide is for mid-level professionals, career switchers, and experienced analysts who want to advance and land competitive roles.

Data Analyst Interview Loop

Most Tier 1 tech companies follow a structured loop when hiring data analysts.

• Recruiter Screen: Covers basic qualifications and sometimes includes surprise SQL questions (e.g., What’s the difference between RANK and DENSE_RANK?).
• Hiring Manager Interview: Ranges from casual fit-based discussions to deep dives into SQL or case problems.
• Technical Round: Can include asynchronous SQL tests or live coding challenges.
• Business Case Round: Assesses your analytical thinking and business acumen with open-ended scenarios.
• Behavioral & Culture Fit Interview: Focuses on collaboration, ambiguity, and alignment with company values.

Each round may vary in depth depending on the company and team.

SQL

SQL is non-negotiable in analytics interviews.

SQL is tested in nearly every round and is essential for daily work.

Sample questions:

• Write a query to calculate total revenue per customer.
• What’s the difference between LEFT JOIN and INNER JOIN?
• Use window functions to rank customer purchases by date.
• What does COALESCE() do in a query?
• How would you calculate a rolling average over 7 days?

Interviewers are evaluating your syntax and structure, communication, edge case handling, and ability to derive insights from the output.

These are the types of SQL questions to expect in each round:

Excel & Google Sheets

Even at big tech companies, spreadsheet tools matter.

Spreadsheets are often used in take-home cases or live walkthroughs.

Sample questions:

• Build a pivot table to group customer data by region
• What’s the difference between VLOOKUP and INDEX-MATCH? Which would you use and why?
• Given an Excel file, calculate ROI and share 2-3 insights for the marketing team.

Data Visualization & Dashboarding

Data storytelling is a core part of communicating your insights.

Candidates must know how to build and explain dashboards using tools like Tableau, Power BI, or Looker.

Sample questions:

• Which chart type would you use to show retention trends?
• How would you redesign a cluttered dashboard to make it stakeholder-friendly?
• Explain how you would visualize an A/B test result.

These are the types of data visualization questions to expect in each round:

Example Data Visualization Question

Question: How do you handle conflicting requests from stakeholders?

What interviewers are testing:

• Can you manage the scope without losing trust?
• Are you able to negotiate trade-offs while staying business-focused?
• Do you think like a product owner in balancing needs, impact, and feasibility?

Common pitfalls:

• "I’ll try to incorporate all feedback and merge ideas into the same dashboard."

This is well-meaning, but leads to cluttered, unfocused dashboards. Doesn’t demonstrate prioritization.

What to include:

• "I first clarify what’s in-scope in the PRD. If it's a reporting vs. exploration conflict, I split into two views."
• Reference prioritization: "I use the effort-impact tradeoff to decide what goes into the MVP."
• Share a real trade-off: "The product team wanted a monthly view, and the ops wanted a weekly view. I built both using toggle controls after confirming feasibility."

Data Analysis Process

Interviewers want to see how you approach messy, real-world data problems.

This includes your ability to:

• Clarify the business question
• Collect and clean the data
• Choose the right metrics and framework
• Communicate clear, actionable insights

Sample questions:

• You’re handed an incomplete dataset on customer churn. How do you proceed?
• How do you decide which KPIs matter in a marketing funnel analysis?

Statistics & Experimentation

You don’t need to be a statistician, but a working understanding of the following is essential:

• Probability distributions
• Hypothesis testing
• Confidence intervals
• Regression
• A/B testing frameworks

Sample questions:

• What statistical test would you use to compare two user groups?
• How do you determine the sample size for an A/B test?
• What is the difference between Type I and Type II errors?
• A $5 discount coupon is given to N riders. The probability of using a coupon is P. What is the expected cost for the company?

These are the types of statistics and experimentation questions to expect in each round:

Python

Python prep is only relevant if the job description calls for it. If it does, expect basic manipulation tasks using pandas.

Sample questions:

• Write a Python function to remove outliers from a dataset using the z-score.
• Use pandas to group website sessions by user and calculate session duration.

Business Case and Analytical

Top companies often include case interviews or live prompts to test how you approach ambiguous business questions.

• Business Performance Evaluation: Sales dropped 25%—how would you investigate?
• Operational Efficiency: How would you optimize delivery times using data?
• Product/Feature Analysis: What metrics would you analyze to understand a feature’s adoption?
• Growth Strategy: Where would you look for opportunities to improve user retention?

Sample questions:

• Here’s a revenue table by region—what stands out?
• Here’s CAC, conversion rate, and revenue by client—what stands out?
• Tell me about a time you discovered a trend by drilling into data.
• How would you use cohorts to identify retention issues?

Use frameworks like PACE (Problem, Approach, Calculation, Explanation) to structure your responses.

Behavioral

Behavioral rounds assess how you collaborate, communicate, and adapt. Structure responses using frameworks like STAR or PACE.

Sample questions:

• Tell me about a time you handled conflicting stakeholder priorities.
• Describe a time your analysis was wrong—what did you learn?
• How do you handle working with incomplete data?
• Talk about a time you influenced a product decision with data.
• Share a story where your communication made a measurable impact.

Hiring managers look for ownership, cross-functional collaboration, and the ability to tie data work to business outcomes.

Take-Home Case Studies

Take-home exercises are common and simulate real analytics work. You'll be evaluated on:

• Your approach to structuring the problem
• Clarity and insight into your findings
• Visualization and storytelling

Tips for success:

• Start with clarifying questions and assumptions
• Prioritize metric selection
• Present findings with business impact in mind

Example Take-Home Case Study Question

Question: Analyze customer retention for an e-commerce platform using the dataset provided. Identify potential drop-off points and propose two strategies for increasing retention. Include visualizations and a 5-minute executive summary.

Interview Prep

The best candidates don’t just prepare hard. They prepare strategically.

Personalize Your Prep

1. Read the job description carefully. Focus your time where the role is most important: SQL, dashboards, or business cases. Don’t study everything equally.
2. Start with your weakest area. If SQL scares you, prioritize that first. If you’re great at Python but struggle to structure business cases, dive into mock cases and watch sample interviews.
3. Layer your practice.
   • Week 1–2: Review frameworks, watch expert mocks, and complete drills.
   • Week 3: Practice full mock interviews (live or recorded).
   • Week 4: Focus on timing, polish, and interview readiness.

We recommend:

• One focused interview question per day.
• Track your answers and self-review or get peer feedback.
• Use AI or a friend as a mock interviewer to simulate pressure.

Check out our other resources to help you on your journey:

• 📹 Data Analytics Interview Prep Course
• ❓Top Data Analyst Interview Questions
• 📖 Company-Specific Interview Guides
• 💬 Private 1:1 Interview Coaching