Data Analyst Interview Guide 2025-2026: SQL, Python, and Business Analytics

Data Analyst roles in India are among the most competitive in tech — and the interviews are evolving fast. Product companies now expect SQL mastery, Python for data manipulation, statistical literacy, A/B testing design, and the ability to translate data into business decisions. This guide covers every layer of a senior data analyst interview with scenario-based questions, worked examples, and expert coaching tips from RVK Tech specialists.

The Modern Data Analyst Interview: What's Changed

Five years ago, data analyst interviews asked basic SQL and Excel. Today, senior data analyst roles at product companies like Meesho, Flipkart, Swiggy, Razorpay, and Zomato require advanced SQL window functions, Python pandas at scale, experiment design and A/B testing, funnel analysis, cohort analysis, dashboard storytelling, and the ability to frame business problems as analytical questions.

Companies run 4–6 rounds: SQL coding test (timed), Python/analytical case study, statistics round, business case/presentation, SQL deep dive, and culture/behavioral round. This guide prepares you for all of them.

Data Analyst Interview Preparation Plan: 4 Weeks

Round 1: Advanced SQL — The Most Important Test

SQL is the core of every data analyst assessment. The questions that filter candidates are window functions — most candidates struggle here.

Window Functions: The Number-One Interview Topic

• ROW_NUMBER() vs RANK() vs DENSE_RANK(): Know the difference when there are ties. ROW_NUMBER() never repeats, RANK() skips numbers after ties, DENSE_RANK() never skips. Classic question: "get the second-highest salary per department."
• LAG() and LEAD(): For comparing a row to the previous or next row. Use case: month-over-month revenue change, time between orders.
• Running totals and moving averages: SUM() OVER (PARTITION BY ... ORDER BY ... ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)
• FIRST_VALUE() and LAST_VALUE(): With ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING frame specification.
• NTILE(n): Divide rows into n equal buckets. Use case: customer revenue quartile analysis.

Real SQL Scenario: User Retention Analysis

"Write a SQL query to find the 30-day retention rate for users who signed up in January 2025."

1. Find all users who signed up in Jan 2025 from the users table
2. LEFT JOIN to events table to find users who had at least one event 30 days after signup
3. Retention rate = COUNT(users with event at day 30) / COUNT(total Jan 2025 signups)
4. Use DATEDIFF and window function or CTE for clean, readable structure

Round 2: Python Data Analysis with Pandas

Python assessments in data analyst interviews focus on pandas operations on messy real-world datasets, not algorithmic coding.

Essential Pandas Operations (Interview Tested)

• groupby + aggregation: Multi-level groupby, named aggregations with .agg({'col': ['mean', 'sum', 'count']}), reset_index.
• merge and join: Inner, left, right, outer joins. Merging on multiple keys. Handling duplicate column names with suffixes.
• apply and lambda: Row-wise operations, creating new columns from multiple existing columns.
• String operations: .str.contains(), .str.extract() with regex, .str.split(), handling mixed types.
• Date/time operations: pd.to_datetime(), .dt accessor, resample() for time-series aggregation.
• Missing data: isna(), fillna(), dropna(), forward-fill and backward-fill strategies.

Python Analysis Scenario: Cohort Retention

"Given a dataframe with user_id, signup_date, and activity_date — calculate monthly cohort retention for the last 6 months."

This type of question tests: date manipulation, groupby on time periods, multi-level indexing, and pivot tables. It's a 20–30 minute exercise and is asked at most product company data analyst interviews.

Round 3: Statistics and A/B Testing

Statistics and experiment design are the highest-leverage skills for product analytics roles. Interviewers probe your ability to design valid experiments, not just run t-tests.

A/B Testing: What Interviewers Really Ask

• How do you determine sample size? Effect size, statistical power (typically 80%), significance level (α = 0.05). Use a power calculator.
• What is p-value? The probability of observing data at least as extreme as the current data, assuming the null hypothesis is true. NOT the probability that the null is true.
• Type I vs Type II error: False positive (rejecting null when it's true) vs false negative (failing to reject null when it's false).
• Common A/B testing mistakes: Peeking (stopping the test early when results look good), multiple testing problem (testing too many variants), novelty effect (users behave differently because something is new).
• What do you do when your A/B test shows a significant uplift in the primary metric but a drop in a secondary metric? This is a product judgment question — look at guardrail metrics and net impact.

Round 4: Business Analytics and Metrics Frameworks

Senior data analyst interviews include open-ended business case questions. These test your analytical thinking, not just technical skills.

Product Metrics Questions

• "Daily active users dropped 15% last week. How would you investigate?" — Check data pipeline first (instrumentation bug?). Then segment by platform, geography, user cohort, feature usage. Compare to business events (marketing campaign change, release). Look at acquisition vs engagement vs retention split.
• "How would you define the North Star Metric for a food delivery app?" — Primary: number of orders delivered successfully. Guardrail metrics: delivery time, cancellation rate, driver utilization, restaurant partner NPS.
• "How would you design a dashboard for the Head of Growth?" — Identify their decisions, not all possible metrics. Focus on actionable metrics: weekly active users trend, D7/D30 retention, acquisition cost by channel, conversion funnel drop-offs.

Salary Expectations for Data Analysts in India (2025-2026)

• Fresher with SQL + Python: ₹5L–₹10L in service companies, ₹8L–₹14L in product companies
• 2–4 years, analytics + BI tools: ₹10L–₹20L depending on domain
• 4–7 years, senior data analyst + A/B testing: ₹20L–₹35L at product companies
• Product analytics specialist at top companies: ₹35L–₹55L (Swiggy, Razorpay, Zepto)