Flipkart Data Analyst Interview Questions 2026: SQL, Cases & Solutions

Cracking a Data Analyst or Business Intelligence role at Flipkart requires more than memorizing basic SQL syntax. As one of the premier employers in the E-Commerce & Quick Commerce space, Flipkart's interview loops are designed to test your ability to translate ambiguous business problems into rigorous, optimized queries and actionable product insights.

In this comprehensive 2026 preparation guide, we break down the 4-stage interview process, explore core SQL problem patterns with working PostgreSQL solutions, analyze key product metrics, and review verified compensation benchmarks.

The Flipkart Data Analyst Interview Process (4 Rounds)

Flipkart structures its analytics hiring loop into four distinct stages:

Round 1: Online Technical Assessment (60 Mins)

3 SQL coding problems (medium-to-hard) covering aggregations with HAVING, multi-table joins, and window functions, plus 10 MCQs on business statistics and probability.

Round 2: Live SQL Machine Coding & Data Wrangling (60 Mins)

Live problem-solving with a Senior Analyst on complex e-commerce order/catalog schemas. Evaluates edge case handling (NULL prices, cancelled orders, returns) and query performance.

Round 3: Product Sense & Funnel Diagnostics (45–60 Mins)

Business case study round: diagnosing checkout funnel drop-offs, identifying dead stock, calculating customer acquisition cost (CAC) and customer lifetime value (LTV).

Round 4: Bar Raiser & Hiring Manager Round (45 Mins)

Deep dive into past portfolio projects, cross-functional stakeholder communication, and cultural alignment with Flipkart's customer-first values.

Top Flipkart SQL Interview Problems & Solutions

Here are realistic SQL problems reflecting the exact domain shapes tested in Flipkart technical rounds:

Problem 1: Top 3 Selling Products per Category (Window Functions)

Business Scenario:
Flipkart category managers need to identify the top 3 revenue-generating products in each category during the Big Billion Days sale, handling revenue ties cleanly.

Table Schema:

orders(order_id, product_id, category_id, sale_price, quantity, order_date, status)

Optimal SQL Solution (PostgreSQL):

WITH product_revenue AS (
    SELECT 
        category_id,
        product_id,
        SUM(sale_price * quantity) AS total_revenue
    FROM orders
    WHERE status = 'Delivered'
      AND order_date >= '2026-01-01'
    GROUP BY category_id, product_id
),
ranked_products AS (
    SELECT 
        category_id,
        product_id,
        total_revenue,
        DENSE_RANK() OVER (
            PARTITION BY category_id 
            ORDER BY total_revenue DESC
        ) AS revenue_rank
    FROM product_revenue
)
SELECT 
    category_id,
    product_id,
    total_revenue,
    revenue_rank
FROM ranked_products
WHERE revenue_rank <= 3
ORDER BY category_id, revenue_rank;

Step-by-Step Logic Breakdown:

1. First CTE aggregates gross delivered revenue per product within each category.
2. Second CTE uses DENSE_RANK() partitioned by category_id to ensure tied revenue amounts receive identical rank without skipping rank numbers.
3. Outer query filters revenue_rank <= 3 to extract top performers.

[!TIP] Test your solution in Topfolio's SQL sandbox: Write and run real SQL queries against this exact schema with instant PostgreSQL grading.
👉 Solve this question live in the Interactive Sandbox →

Problem 2: Identify Dead Inventory (Anti-Join / NOT EXISTS)

Business Scenario:
Warehouse supply chain teams need to find all product SKUs listed in the inventory catalog that have recorded ZERO orders in the last 90 days.

Table Schema:

products(product_id, product_name, category_id, stock_quantity)
orders(order_id, product_id, order_date)

Optimal SQL Solution (PostgreSQL):

SELECT 
    p.product_id,
    p.product_name,
    p.stock_quantity
FROM products p
LEFT JOIN orders o 
    ON p.product_id = o.product_id 
   AND o.order_date >= CURRENT_DATE - INTERVAL '90 days'
WHERE o.order_id IS NULL
  AND p.stock_quantity > 0
ORDER BY p.stock_quantity DESC;

Step-by-Step Logic Breakdown:
A LEFT JOIN preserves every product in the catalog. Putting the 90-day filter in the ON clause ensures orders outside the window do not discard the product. The WHERE o.order_id IS NULL filters strictly for products with no matching orders.

[!TIP] Test your solution in Topfolio's SQL sandbox: Write and run real SQL queries against this exact schema with instant PostgreSQL grading.
👉 Solve this question live in the Interactive Sandbox →

Problem 3: Customer RFM Segmentation (Recency, Frequency, Monetary)

Business Scenario:
Segment Flipkart customers into VIP, Loyal, and At-Risk tiers based on their order count, total spend, and days since last purchase.

Table Schema:

orders(order_id, customer_id, order_date, total_amount, status)

Optimal SQL Solution (PostgreSQL):

WITH customer_metrics AS (
    SELECT 
        customer_id,
        MAX(order_date) AS last_order_date,
        COUNT(DISTINCT order_id) AS order_frequency,
        SUM(total_amount) AS monetary_value
    FROM orders
    WHERE status = 'Delivered'
    GROUP BY customer_id
)
SELECT 
    customer_id,
    order_frequency,
    monetary_value,
    CURRENT_DATE - last_order_date AS days_since_last_order,
    CASE 
        WHEN monetary_value >= 50000 AND (CURRENT_DATE - last_order_date) <= 30 THEN 'Champions / VIP'
        WHEN order_frequency >= 10 AND (CURRENT_DATE - last_order_date) <= 60 THEN 'Loyal Core'
        WHEN monetary_value >= 30000 AND (CURRENT_DATE - last_order_date) > 90 THEN 'At Risk / Churning'
        ELSE 'Standard'
    END AS customer_segment
FROM customer_metrics
ORDER BY monetary_value DESC;

Step-by-Step Logic Breakdown:
Aggregates customer order patterns into RFM metrics, calculates days since last purchase, and uses a multi-branch CASE WHEN to assign business cohorts.

[!TIP] Test your solution in Topfolio's SQL sandbox: Write and run real SQL queries against this exact schema with instant PostgreSQL grading.
👉 Solve this question live in the Interactive Sandbox →

Key Product Metrics & Case Studies at Flipkart

In the product sense round, interviewers will ask you to define metrics and diagnose anomalies. Be prepared for questions such as:

1. North Star Metric Definition: What is the primary North Star metric for Flipkart's core business vertical, and what are 2 counter-metrics to ensure quality is not sacrificed for growth?
2. Funnel Drop-Off Diagnosis: "Conversion dropped by 8% week-over-week in Bengaluru. How would you structure your diagnostic investigation?"
3. A/B Experiment Design: How would you design an experiment to test a new recommendation algorithm without cannibalizing organic merchant search?

2026 Flipkart Data Analyst Salary Bands (India)

Based on verified market submissions, here is how compensation is structured at Flipkart:

For a detailed breakdown of how Flipkart compares to IT Services, GCCs, and Tier-1 tech firms, see our comprehensive Data Analyst Salary Guide 2026 and the India Data Roles Salary Progression Guide.