Data Analyst Interview Preparation India 2026

The Typical Interview Structure in India

Most Indian companies run a 3–4 round process for data analyst roles. Understanding the structure lets you prepare each round differently rather than doing one giant generic prep.

Startups often compress to 2-3 rounds. Large IT services companies (TCS, Infosys) sometimes add an online aptitude test before Round 1.

SQL Interview Questions — What Actually Gets Asked

SQL is tested in almost every data analyst interview in India. The questions fall into predictable categories — knowing them in advance turns the technical round from a surprise into a pattern-matching exercise.

Category 1 — JOINs and Aggregation

-- Classic question: "Find customers who placed an order in Jan 2026 but NOT in Feb 2026"
SELECT DISTINCT o1.customer_id
FROM   orders o1
WHERE  o1.order_month = '2026-01'
  AND  o1.customer_id NOT IN (
         SELECT customer_id
         FROM   orders
         WHERE  order_month = '2026-02'
       );

-- Interviewer follow-up: rewrite using LEFT JOIN (faster on large tables)
SELECT DISTINCT o1.customer_id
FROM   orders o1
LEFT   JOIN orders o2
  ON   o1.customer_id = o2.customer_id
 AND   o2.order_month = '2026-02'
WHERE  o1.order_month = '2026-01'
  AND  o2.customer_id IS NULL;

Category 2 — Window Functions (Most Commonly Tested)

-- "Get the second highest salary per department"
SELECT department, employee_name, salary
FROM (
  SELECT department,
         employee_name,
         salary,
         DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rnk
  FROM   employees
) t
WHERE rnk = 2;

-- "Running total of revenue by month"
SELECT order_month,
       monthly_revenue,
       SUM(monthly_revenue) OVER (ORDER BY order_month) AS cumulative_revenue
FROM   monthly_sales;

-- "Month-over-month revenue growth %"
SELECT order_month,
       monthly_revenue,
       LAG(monthly_revenue) OVER (ORDER BY order_month)          AS prev_month,
       ROUND(
         (monthly_revenue - LAG(monthly_revenue) OVER (ORDER BY order_month))
         / LAG(monthly_revenue) OVER (ORDER BY order_month) * 100, 2
       ) AS growth_pct
FROM   monthly_sales;

Category 3 — Self JOIN and CASE WHEN

-- "Find employees who earn more than their manager"
SELECT e.employee_name, e.salary, m.salary AS manager_salary
FROM   employees e
JOIN   employees m ON e.manager_id = m.employee_id
WHERE  e.salary > m.salary;

-- "Classify orders by value bucket"
SELECT order_id,
       order_value,
       CASE
         WHEN order_value < 500   THEN 'Low'
         WHEN order_value < 2000  THEN 'Medium'
         WHEN order_value < 10000 THEN 'High'
         ELSE                          'Premium'
       END AS value_segment
FROM   orders;

Category 4 — NULL Handling and Date Functions

-- COALESCE replaces NULL with a default
SELECT customer_id,
       COALESCE(email, phone, 'No contact info') AS contact
FROM   customers;

-- Orders placed in the last 30 days
SELECT * FROM orders
WHERE  order_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY);

-- Extract month and year
SELECT YEAR(order_date) AS yr, MONTH(order_date) AS mo, COUNT(*) AS orders
FROM   orders
GROUP  BY 1, 2
ORDER  BY 1, 2;

Python Interview Tasks — Pandas is the Whole Bar

For data analyst roles (not data scientist), Python interviews focus entirely on pandas. You will not be asked to implement algorithms from scratch. The test is: given a messy DataFrame, can you clean, transform, and summarise it correctly?

Task 1 — Clean and Summarise a Sales Dataset

import pandas as pd

df = pd.read_csv('sales.csv')

# Step 1 — inspect
print(df.shape, df.dtypes, df.isnull().sum())

# Step 2 — fix types
df['order_date'] = pd.to_datetime(df['order_date'])
df['revenue']    = pd.to_numeric(df['revenue'], errors='coerce')

# Step 3 — clean strings
df['city']     = df['city'].str.strip().str.title()
df['category'] = df['category'].str.lower()

# Step 4 — remove duplicates
df = df.drop_duplicates(subset='order_id', keep='first')

# Step 5 — monthly revenue summary
df['month'] = df['order_date'].dt.to_period('M')
summary = (df.groupby(['month', 'category'])['revenue']
             .agg(['sum', 'count', 'mean'])
             .rename(columns={'sum': 'total_rev', 'count': 'orders', 'mean': 'avg_order'})
             .reset_index())
print(summary.head(10))

Task 2 — Merge Two DataFrames and Find Gaps

# Customers who have NOT made a purchase (left join + IS NULL equivalent)
customers = pd.read_csv('customers.csv')   # customer_id, name, city
orders    = pd.read_csv('orders.csv')      # order_id, customer_id, amount

merged = customers.merge(orders[['customer_id']].drop_duplicates(),
                         on='customer_id', how='left', indicator=True)
no_orders = merged[merged['_merge'] == 'left_only'][['customer_id', 'name', 'city']]
print(f"Customers with no orders: {len(no_orders)}")

Task 3 — Pivot Table for a Dashboard View

pivot = df.pivot_table(
    index='category',
    columns='month',
    values='revenue',
    aggfunc='sum',
    fill_value=0
)
# Add a total column
pivot['Total'] = pivot.sum(axis=1)
pivot = pivot.sort_values('Total', ascending=False)
print(pivot)

Case Study Interviews — The SCOPE Framework

Case study rounds feel open-ended but interviewers are evaluating a specific set of behaviours: do you ask good clarifying questions, do you break the problem into parts, do you pick sensible metrics, and can you communicate findings clearly?

Use the SCOPE framework to structure any case study answer:

Example: "Our app's daily active users dropped 15% last week. What happened?"

Take-Home Assignments — How to Stand Out

Take-home assignments are increasingly common for mid-level and senior data analyst roles. Most candidates spend 80% of the time on analysis and 20% on presentation — successful candidates invert this ratio.

Notebook Structure That Works

HR Round — Questions and Answers That Actually Work

HR rounds are eliminators, not selectors. The goal is to clear the bar, not impress. Avoid red flags: badmouthing previous employer, inconsistency between resume and answers, or vague answers to direct questions.

Salary Negotiation — A Practical Script

Most candidates in India leave 10-20% on the table by accepting the first offer or by not negotiating at all. Negotiation is expected — it is not rude or greedy.

The Negotiation Script

30-Day Interview Prep Plan

Interview Patterns by Company Type — Delhi NCR

Frequently Asked Questions

Practice Interviews With a Real Mentor

Evika Academy, Noida Sector 51, offers mock interview sessions with industry mentors who have sat on the other side of the table. Get specific feedback — not generic tips.