60 Data Analyst Interview Questions and Answers (2026)

You have cleared the resume shortlist. Now the real test starts: a SQL round, an Excel or Power BI task, maybe a Python screen, and a case study where nobody gives you the "right" answer.

Most lists of data analyst interview questions give you definitions to memorise. Interviewers in India are not testing definitions. They are testing whether you can pull, clean, explain and defend numbers.

This guide gives you 60 real questions with short model answers, the SQL and DAX you should be able to write on a whiteboard, a framework for case rounds, and a 30-day plan built around what Indian job descriptions ask for in 2026.

Data analyst interview questions are the technical, case and behavioural questions employers use to check whether a candidate can turn raw data into business decisions. In India they usually cover SQL, Excel, Power BI or Tableau, Python with pandas, basic statistics, a business case, and HR questions about projects and communication.

Quick answer: data analyst interview questions to prepare first (November 2026)

• Excel appeared in 81% and SQL in 60% of 328 data analytics job descriptions studied by AccioJob in July 2026, so these two rounds come first.
• Power BI (43%) and Python (41%) were the next most-requested skills in the same AccioJob analysis.
• SQL rounds focus on joins, GROUP BY with HAVING, window functions such as RANK and LAG, CTEs and NULL handling.
• Case rounds test structure: clarify the goal, pick metrics, slice the data, and give a recommendation with numbers.
• Fresher data analyst salaries in India typically range from about ₹4 to ₹8 lakh a year, per Scaler's July 2026 guide citing Glassdoor.

Data Analyst Interview Questions: How Rounds Work in India (2026)

A data analyst interview in India usually has three to five rounds: a recruiter or HR screen, an online test (SQL, Excel and aptitude), a technical interview, a case study or take-home assignment, and a final HR or manager round. Service companies lean on tests, while product companies and GCCs lean on case rounds.

The format changes with the type of employer. Here is the pattern most Indian candidates run into.

Table compiled by Fireblaze AI School from common interview patterns. Round structure varies by company and team.

The hiring backdrop matters too. Naukri JobSpeak reported that white-collar hiring in India grew 2% year on year in September 2026, while AI and ML roles grew 20%. Hyderabad (9%), Chennai (6%) and Bengaluru (4%) led metro growth in its October 2026 report (Naukri).

In plain words: the market is steady, not booming, so the candidate who prepares well gets the offer.

Which Skills to Prepare First: The JD-Weighted Prep Map

Prepare data analyst interview questions in proportion to how often each skill appears in real job descriptions. In AccioJob's July 2026 study of 328 data analytics job descriptions, Excel appeared in 81%, SQL in 60%, Power BI in 43%, Python in 41% and communication skills in 27% (AccioJob).

We turned those numbers into a simple rule for splitting your preparation time, which we call the JD-Weighted Prep Map.

The AccioJob sample comes from companies hiring through one training provider's network, so treat the percentages as directional. SQL gets more prep time than Excel because SQL questions are harder to fake in a live round.

SQL Interview Questions for Data Analysts (1 to 15)

SQL interview questions for data analysts test whether you can pull the right rows, aggregate them correctly and avoid traps like duplicates and NULLs. Expect joins, GROUP BY with HAVING, window functions, CTEs and date logic in almost every SQL round.

1. What is the difference between WHERE and HAVING?

WHERE filters rows before grouping. HAVING filters groups after aggregation.

SELECT city, COUNT(*) AS orders
FROM orders
WHERE order_date >= '2026-01-01'
GROUP BY city
HAVING COUNT(*) > 100;

2. Explain INNER, LEFT, RIGHT and FULL OUTER JOIN.

An INNER JOIN keeps only matching rows from both tables. A LEFT JOIN keeps every row from the left table and fills NULLs where the right table has no match. RIGHT JOIN is the mirror image, and FULL OUTER JOIN keeps all rows from both.

3. How do you find customers who have never placed an order?

Use a LEFT JOIN and keep rows where the right side is NULL.

SELECT c.customer_id, c.name
FROM customers c
LEFT JOIN orders o ON o.customer_id = c.customer_id
WHERE o.order_id IS NULL;

4. What is the difference between COUNT(*), COUNT(column) and COUNT(DISTINCT column)?

COUNT(*) counts all rows. COUNT(column) skips NULLs in that column. COUNT(DISTINCT column) counts unique non-NULL values, which is what you want for "number of customers" rather than "number of orders".

5. Find the second-highest salary in an employees table.

SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);

Mention that DENSE_RANK() handles ties better if the interviewer asks for the "Nth highest".

6. What is the difference between RANK, DENSE_RANK and ROW_NUMBER?

All three number rows within a window. ROW_NUMBER gives unique numbers even for ties. RANK gives ties the same number and skips the next (1, 1, 3). DENSE_RANK gives ties the same number without gaps (1, 1, 2).

7. Find the top 3 products by revenue in each category.

WITH ranked AS (
  SELECT category, product_id, SUM(amount) AS revenue,
         DENSE_RANK() OVER (PARTITION BY category ORDER BY SUM(amount) DESC) AS rnk
  FROM sales
  GROUP BY category, product_id
)
SELECT category, product_id, revenue
FROM ranked
WHERE rnk <= 3;

8. What is a CTE and why would you use one?

A CTE (common table expression) is a named temporary result defined with WITH. It makes long queries readable, lets you reuse a step, and is easier to debug than nested subqueries.

9. Calculate month-on-month revenue growth.

Use LAG() to bring the previous month's value onto the same row.

WITH monthly AS (
  SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue
  FROM orders
  GROUP BY 1
)
SELECT month, revenue,
       ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
             / LAG(revenue) OVER (ORDER BY month), 1) AS mom_growth_pct
FROM monthly;

DATE_TRUNC is PostgreSQL syntax. In MySQL, use DATE_FORMAT(order_date, '%Y-%m-01').

10. How do you find and remove duplicate rows?

Find them with GROUP BY and HAVING COUNT(*) > 1. To keep one copy, number duplicates with ROW_NUMBER() OVER (PARTITION BY the duplicate columns ORDER BY id) and delete rows where the number is greater than 1.

11. How do NULLs behave in SQL?

NULL means unknown, so NULL = NULL is not true. Use IS NULL, and use COALESCE(column, 0) to replace NULLs before arithmetic. Aggregates like SUM and AVG ignore NULLs, which can quietly change an average.

12. Write a running total of daily sales.

SELECT order_date, daily_sales,
       SUM(daily_sales) OVER (ORDER BY order_date) AS running_total
FROM daily_sales;

13. What is the difference between UNION and UNION ALL?

UNION removes duplicate rows across the combined result. UNION ALL keeps every row and is faster, so use it when you know there are no duplicates or you want to keep them.

14. What is the difference between a primary key and a foreign key?

A primary key uniquely identifies each row in a table and cannot be NULL. A foreign key is a column that points to a primary key in another table, which is how joins stay consistent.

15. A query is slow. How would you speed it up?

Check the execution plan, filter early, select only the columns you need, add indexes on join and filter columns, avoid functions on indexed columns in WHERE, and replace repeated subqueries with a CTE or temporary table.

Excel Interview Questions (16 to 25)

Excel interview questions check how fast and accurately you clean, look up and summarise data. Expect lookups, pivot tables, conditional formulas, text and date functions, and basic Power Query, often as a timed task on a sample file.

16. What is the difference between VLOOKUP and XLOOKUP?

VLOOKUP searches the first column of a range and returns a value to its right, and it breaks if columns are inserted. XLOOKUP can look left or right, returns exact matches by default and has a built-in "if not found" argument. Our guide to data analyst course fees is a separate read; for worked lookup examples, practise both formulas on a sample file.

17. When would you use INDEX and MATCH?

Use INDEX-MATCH when XLOOKUP is not available (older Excel versions) and you need a lookup that works in any direction and survives column changes: =INDEX(return_range, MATCH(lookup_value, lookup_range, 0)).

18. What is a pivot table and when do you use it?

A pivot table summarises large data by grouping and aggregating it without formulas, for example total sales by region and month. Use it for quick exploration and for checking your SQL totals.

19. Explain SUMIFS and COUNTIFS.

SUMIFS adds values that meet several conditions, and COUNTIFS counts rows that meet several conditions. Example: =SUMIFS(Sales, Region, "West", Month, "Oct").

20. How do you remove duplicates and clean messy text in Excel?

Use Remove Duplicates on the Data tab, TRIM to remove extra spaces, PROPER or UPPER for consistent case, and Text to Columns or TEXTSPLIT to split values. For repeated cleaning jobs, use Power Query so the steps rerun on new data.

21. What is Power Query?

Power Query is Excel's (and Power BI's) tool to import, clean and reshape data with recorded steps. It is useful when the same monthly file needs the same cleaning every time.

22. What is the difference between relative and absolute cell references?

A relative reference (A1) shifts when you copy a formula. An absolute reference ($A$1) stays fixed. Mixed references ($A1 or A$1) lock only the column or the row.

23. How would you highlight the top 10% of values?

Use Conditional Formatting, then Top/Bottom Rules, then Top 10%. In an interview, mention that you would also check whether outliers are distorting the cut-off.

24. How do you handle errors like #N/A in formulas?

Wrap the formula in IFERROR or IFNA, for example =IFNA(XLOOKUP(...), "Not found"). But first find out why the error happens, because a missing match is often a data quality issue.

25. How would you build a simple dashboard in Excel?

Clean the data into a table, build pivot tables for each KPI, add pivot charts and slicers, and place them on one sheet with the key numbers at the top. Keep it to the metrics the manager asked for.

Power BI Interview Questions (26 to 35)

Power BI interview questions for data analysts focus on data modelling, DAX measures, Power Query and dashboard design. Interviewers want to know whether you can build a model that gives correct numbers, not just pretty visuals.

For a longer bank, read our 75 Power BI interview questions.

26. What is the difference between a calculated column and a measure?

A calculated column is computed row by row when data refreshes and is stored in the model. A measure is computed at query time based on the filters in the visual. Use measures for totals, ratios and anything that should respond to slicers.

27. What is a star schema and why does Power BI prefer it?

A star schema has one central fact table (for example sales) connected to dimension tables (date, product, customer, region). A clean data model like this keeps relationships simple, makes DAX easier to write and improves performance.

28. Explain the CALCULATE function.

CALCULATE evaluates an expression under modified filters. Example: West Sales = CALCULATE(SUM(Sales[Amount]), Region[Region] = "West").

29. What is the difference between SUM and SUMX?

SUM adds a single column. SUMX iterates over a table row by row and adds the result of an expression, such as quantity multiplied by price.

30. How do you calculate year-to-date sales in DAX?

Use a proper date table marked as a date table, then the TOTALYTD function: Sales YTD = TOTALYTD(SUM(Sales[Amount]), 'Date'[Date]). For an Indian financial year ending 31 March, add the year-end argument: TOTALYTD(SUM(Sales[Amount]), 'Date'[Date], "31/3").

31. What is row-level security?

Row-level security (RLS) restricts which rows a user can see, for example a regional manager seeing only their region. You define roles with DAX filters in Power BI Desktop and assign users in the Power BI Service.

32. Import mode vs DirectQuery: which would you choose?

Import mode loads data into Power BI and is fastest for most reports. DirectQuery queries the source live and suits very large or near-real-time data, but it is slower and limits some DAX features.

33. How do you improve a slow Power BI report?

Remove unused columns, reduce cardinality, use a star schema, prefer measures over calculated columns, limit visuals per page, and check the Performance Analyzer to find the slowest visual.

34. What makes a good dashboard?

A good dashboard answers a few business questions at a glance: KPIs at the top, trends next, details last, consistent colours and clear titles.

35. How do you use Copilot or AI tools in Power BI, and how do you check their output?

Microsoft offers Copilot in Power BI to suggest DAX, summarise reports and draft visuals. A good answer says you use it to speed up first drafts, then verify every measure against a known total, because AI-generated DAX can be syntactically right and logically wrong. Interviewers increasingly ask how you validate AI output, not whether you use it.

Python Interview Questions for Data Analysts (36 to 45)

Python interview questions for data analysts focus on pandas for cleaning and aggregation, basic data structures, and simple visualisation. You are rarely asked algorithm puzzles; you are asked to load, clean, group and explain data.

36. What is the difference between a list, a tuple, a set and a dictionary?

A list is ordered and changeable. A tuple is ordered but cannot be changed. A set stores unique items without order. A dictionary stores key-value pairs for fast lookup.

37. How do you read a CSV and check its structure in pandas?

import pandas as pd
df = pd.read_csv("orders.csv")
df.shape        # rows, columns
df.info()       # data types and non-null counts
df.describe()   # summary statistics
df.head()

38. How do you handle missing values in pandas?

Missing values show up as NaN in pandas. Find them with df.isna().sum(), as the pandas documentation explains. Then decide per column: drop rows with dropna(), fill with a value or the median using fillna(), or flag them in a new column. Say why you chose each option.

39. How do you group and aggregate data?

df.groupby("city").agg(orders=("order_id", "count"),
                       revenue=("amount", "sum")).sort_values("revenue", ascending=False)

40. What is the difference between merge, join and concat?

merge combines DataFrames on columns, like a SQL join. join combines on the index. concat stacks DataFrames vertically or side by side.

41. How do you remove duplicates?

Use df.duplicated() to find them and df.drop_duplicates(subset=["order_id"], keep="first") to remove them.

42. What is the difference between loc and iloc?

loc selects by labels (row index names and column names). iloc selects by integer position.

43. How do you detect outliers in Python?

Use the IQR rule: values below Q1 minus 1.5 times IQR or above Q3 plus 1.5 times IQR. Plot a box plot to see them. Then check whether they are errors or real, important cases before removing anything.

44. What is the difference between apply() and vectorised operations?

Vectorised operations like df["a"] * df["b"] work on whole columns at once and are much faster. apply() runs a function row by row or column by column and is slower, so use it only when no vectorised method exists.

45. Which libraries would you use to visualise data?

Matplotlib for basic control, Seaborn for quick statistical charts, and Plotly for interactive charts. In a data analyst job, the final dashboard often lives in Power BI or Tableau, while Python handles cleaning and analysis.

Statistics Interview Questions (46 to 51)

Statistics interview questions for data analysts test practical concepts: averages and spread, correlation, hypothesis testing and A/B tests. Explain each with a business example instead of a formula.

46. When would you use the median instead of the mean?

Use the median when data is skewed or has outliers. Average order value can be pulled up by a few bulk orders, so the median gives a truer picture of a typical customer.

47. What is the difference between correlation and causation?

Correlation means two variables move together. Causation means one causes the other. Ice cream sales and AC sales both rise in summer, but one does not cause the other; the heat drives both.

48. What is a p-value?

A p-value is the probability of seeing a result at least this extreme if there were really no effect. A p-value below the chosen threshold (often 0.05) means the result is unlikely to be random chance alone.

49. How would you design an A/B test?

A/B testing compares two versions on live users. Define one metric (for example checkout conversion), split users randomly into control and variant, fix the sample size and test duration in advance, run the test without peeking, and check significance and practical impact before recommending a rollout.

50. What are Type I and Type II errors?

A Type I error is a false positive: you conclude a change worked when it did not. A Type II error is a false negative: you miss a real effect.

51. What is standard deviation in simple terms?

Standard deviation measures how spread out values are around the average. Two stores can both average ₹50,000 daily sales, but the one with a higher standard deviation has much less predictable days.

Case Study Data Analyst Interview Questions (52 to 55)

Case study questions test how you structure an unclear business problem. Use the CLEAR framework: Clarify the goal, List the metrics, Explore the data by segments, Analyse causes, and Recommend an action with numbers.

52. Orders on a food delivery app fell 15% last week. How would you investigate?

Confirm the data is correct first (tracking or pipeline issue?). Then split by city, platform, user type, restaurant and time slot to find where the drop is concentrated. Check external factors such as festivals, rain or a competitor offer, and internal changes such as price, app release or delivery fee.

53. How would you measure the success of a new feature?

Pick a primary metric tied to the feature's goal, a few guardrail metrics (like cancellations or app crashes), compare against a control group or a before period, and report both the effect size and whether it is statistically significant.

54. A manager wants a dashboard "by tomorrow". What do you do?

Ask which decisions the dashboard should support and which three to five KPIs matter most. Build a simple first version with clean, verified numbers, share it, and improve it after feedback. Say clearly what is not included yet.

55. How would you handle a take-home assignment?

State your assumptions, clean the data and document what you changed, answer the exact questions asked, and present three to five insights with one clear recommendation. A short summary slide at the start helps busy reviewers.

HR and Behavioural Data Analyst Interview Questions (56 to 60)

HR and behavioural questions check communication, ownership and how you handle pressure. Answer with the STAR method: Situation, Task, Action, Result, and keep each answer under two minutes.

56. Tell me about yourself.

Give a 60 to 90 second story: your background, how you got into data, one or two projects with a measurable result, and why this role fits. Do not repeat your resume line by line.

57. Walk me through a data project you are proud of.

Explain the business question, the data source, how you cleaned it, the tools you used (SQL, Power BI, Python), the main insight and what changed because of it. Keep numbers honest and be ready for follow-up questions on every step.

58. How do you explain technical findings to non-technical people?

Lead with the conclusion, use one simple chart, and translate numbers into business terms ("we are losing about one in five customers at the payment step") instead of statistical jargon.

59. Tell me about a time your analysis was wrong.

Pick a real, small mistake, explain how you found it, how you fixed it and what check you added afterwards. Interviewers want to see that you validate your work.

60. Why should we hire you as a data analyst?

Connect your skills to their job description: name the tools they asked for, one project that proves each, and how you learn new tools quickly. End with interest in their specific domain, such as fintech, e-commerce or healthcare.

Weak Answer vs Strong Answer: 5 Common Mistakes

The most common data analyst interview mistakes are memorised definitions, unverified numbers, no business context, tool lists without outcomes, and silence while solving. This table shows how to fix each.

30-Day Prep Plan for Data Analyst Interview Questions

A 30-day plan is enough to prepare for most entry-level data analyst interviews if you already know the basics. Spend about two hours a day and follow the JD-Weighted Prep Map above.

If you need projects to talk about, start with our data analytics projects for beginners. If you are still choosing the career, read our guide to data analyst vs data scientist roles, and the data analyst salary in India guide.

Should you join a course to prepare?

A course is not required, but it helps if you want mentor feedback, mock interviews and real datasets on a fixed timeline. Compare options on project work, interview practice and updated tools. Our breakdown of data analyst course fees shows what each format costs.

If you want guided preparation, Fireblaze AI School's AI-Powered Data Analytics certification covers Excel, SQL, Power BI, Python and AI tools with project reviews and mock interview rounds.

Now It's Your Turn

You now have 60 data analyst interview questions, the code to back up your answers, the CLEAR framework for case rounds and a 30-day plan. The candidates who get offers are rarely the ones who know the most definitions; they are the ones who check their numbers and explain what the numbers mean.

Start with SQL today. Pick five questions from this list, write the queries without looking, and say the "so what" out loud.

Which round do you find hardest? Practise that one first.

FAQ

What are the most common data analyst interview questions?

The most common data analyst interview questions cover SQL joins and GROUP BY, window functions, Excel lookups and pivot tables, Power BI measures and DAX, pandas data cleaning, basic statistics like mean vs median, a business case, and "tell me about a project".

How should I prepare interview questions of data analyst roles as a fresher?

Focus on SQL and Excel first, then Power BI and Python, because Excel (81%) and SQL (60%) appeared most often in AccioJob's 2026 study of 328 job descriptions. Build two or three projects you can explain end to end, and practise mock interviews out loud.

Is SQL enough to crack a data analyst interview?

SQL alone is rarely enough. Most Indian employers also test Excel, a BI tool such as Power BI or Tableau, and your ability to explain insights. Python is increasingly expected for higher-paying roles.

How many rounds are there in a data analyst interview?

Most data analyst interviews in India have three to five rounds: a screening call, an online test, a technical interview, a case study or take-home assignment, and a final HR or manager round.

What is the salary of a fresher data analyst in India in 2026?

Fresher data analyst salaries in India typically range from about ₹4 to ₹8 lakh per year, according to Scaler's guide updated in July 2026, which cites Glassdoor data. Pay varies by city, company type and skills.

Are AI tools like Copilot asked about in data analyst interviews?

Yes, more interviewers now ask how you use AI tools such as Copilot in Power BI or ChatGPT for SQL. The best answer explains how you verify AI output against known totals before sharing results.

Sources

1. Naukri JobSpeak: White-collar hiring holds steady in September; AI/ML roles continue growth streak, October 2026
2. AccioJob: Most In-Demand Data Analytics Skills in 2026 (Based on 328 Real Job Descriptions), July 2026
3. Scaler: Data Analyst Salary 2026: Freshers, Experienced, India and US Breakdown, updated July 2026
4. Microsoft Learn: Overview of Copilot for Power BI, 2026
5. Microsoft Learn: TOTALYTD function (DAX), 2026
6. pandas documentation: Working with missing data, 2026