Top 15 Data Analyst Interview Questions (And How to Answer Them)

Data analyst interviews test a specific combination: technical skills (SQL, statistics, Excel) plus the ability to translate numbers into business decisions. You'll likely face a mix of technical questions, case studies, and behavioral scenarios.

The biggest mistake candidates make? Over-focusing on the technical and under-preparing for the "so what" — explaining what the data means and what the business should do about it.

Here's what to expect.

Technical Questions

1. "Write a SQL query to find the top 5 customers by revenue in the last 90 days."

SQL is non-negotiable. Even if you use Python or R daily, interviewers test SQL because it's universal.

What they're checking: JOINs, aggregation, filtering, ordering. This specific question tests GROUP BY, ORDER BY, LIMIT, and date filtering.

Tip: Talk through your approach before writing. "I'd join the orders table with customers, filter for the last 90 days, aggregate revenue per customer, and sort descending."

2. "What's the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN?"

Fundamental SQL knowledge. If you hesitate here, it's a red flag.

Quick answer: INNER returns only matching rows. LEFT returns all rows from the left table plus matches from the right (nulls where no match). FULL OUTER returns all rows from both tables.

Bonus: Give a practical example. "I'd use a LEFT JOIN to find all customers including those who haven't placed an order yet."

3. "How would you handle missing data in a dataset?"

They want to see that you don't just delete rows blindly.

Framework: 1. Understand why it's missing (random, systematic, or data pipeline issue?) 2. Assess the impact (what % is missing? Is it concentrated in one segment?) 3. Choose a strategy: imputation (mean, median, model-based), flagging, or exclusion — depending on the use case 4. Document your decision

4. "Explain the difference between correlation and causation."

Classic statistics question. Everyone knows the textbook answer — differentiate yourself with a real example.

Example: "Ice cream sales and drowning rates are correlated — both go up in summer. But ice cream doesn't cause drowning. The confounding variable is temperature."

Then connect it to business: "This matters because if we see that users who use feature X have higher retention, we can't assume X causes retention — maybe power users just adopt everything."

5. "What statistical test would you use to determine if a new feature increased conversion?"

They want to know you can design an experiment properly.

Answer: A/B test with a two-sample t-test or chi-squared test (depending on the metric type). Mention: sample size, significance level (p < 0.05), and checking for confounders.

Business Case Questions

6. "Revenue dropped 15% last month. Walk me through how you'd investigate."

The most common case question for data analysts. Be systematic, not scattered.

Framework: 1. Confirm the metric definition (is this total revenue, ARPU, or something else?) 2. Time comparison (vs. prior month, vs. same month last year — is it seasonal?) 3. Segment the data: by product, region, customer cohort, channel 4. Look for anomalies: did one big client churn? Did pricing change? Was there an outage? 5. Check external factors: competitor launch, market event, holiday 6. Present findings with a recommendation

7. "How would you measure the success of a marketing campaign?"

Show you think beyond clicks and impressions.

Layers: - Awareness: reach, impressions - Engagement: CTR, time on page - Conversion: sign-ups, purchases, cost per acquisition - Retention: did campaign-acquired users stick around? - ROI: revenue from campaign vs. cost

"The right metric depends on the campaign goal. A brand awareness campaign? Reach and recall. A performance campaign? CPA and ROI."

8. "We have 10 million rows of transaction data. How would you find patterns in customer behavior?"

They want to see your analytical workflow, not just tool names.

Approach: 1. Start with exploratory analysis — distributions, outliers, time trends 2. Segment customers (RFM analysis: Recency, Frequency, Monetary) 3. Look for cohort patterns (do users acquired in Q1 behave differently than Q3?) 4. Visualize key findings before going deeper 5. Identify actionable patterns ("customers who buy twice in 30 days have 3x lifetime value")

9. "How would you explain a complex analysis to a non-technical stakeholder?"

Critical skill. Many analysts can do the work but can't communicate it.

Approach: Start with the conclusion ("we should do X"), then the key evidence (1-2 data points), then offer the details if they want to go deeper. Never start with methodology.

Behavioral Questions

10. "Tell me about a time your analysis changed a business decision."

This is where you prove impact, not just skill. Pick an example where your work directly influenced what the company did.

Structure: What was the question → what did you find → what decision was made → what was the outcome?

11. "Describe a time you found an error in someone else's data or report."

They're testing diplomacy and attention to detail. Show you caught it, communicated it respectfully, and helped fix it — not that you publicly embarrassed someone.

12. "How do you prioritize when you have multiple data requests from different teams?"

Real-world chaos. Show you have a system.

Answer: "I assess urgency (deadline-driven vs. exploratory), impact (revenue-affecting vs. nice-to-know), and effort (quick query vs. multi-day analysis). I communicate timelines clearly and push back when needed."

13. "What tools do you use for data analysis?"

Be specific: name the tools and what you use each for. "SQL for querying, Python with pandas for analysis, Tableau for dashboards, Excel for quick ad-hoc work." Don't just list — show depth.

14. "Where do you see the field of data analytics going?"

They want curiosity and awareness. Mention AI/ML augmenting analysis, the shift toward real-time analytics, data democratization (self-serve tools), and the growing importance of data literacy across organizations.

15. "What questions do you have for us?"

Ask about: - What does the data stack look like? (Warehouse, BI tools, ETL) - Who are the primary stakeholders for the analytics team? - What's the biggest unanswered question the team is working on? - How are data projects prioritized?

What Makes Data Analyst Candidates Stand Out

• Business intuition. You don't just pull numbers — you explain what they mean.
• Structured thinking. You approach problems methodically, not randomly.
• Communication. You can explain findings to people who don't speak SQL.
• Curiosity. You dig deeper when something looks off, instead of reporting the number at face value.
• Honesty about uncertainty. "The data suggests X, but I'd want to validate with Y before we commit."

Your Data Analyst Interview Is Unique

These questions cover the common patterns, but your specific interview will focus on the tools, domain, and business problems in the job listing.

Want questions tailored to your exact role? Paste the job description at PasteJob and get a personalized prep guide with likely questions, answer frameworks, and hidden signals — in 15 seconds.