01
How do you calculate a running total?
Show answer
SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.
Myntra Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Myntra hiring for Data Analytics roles
Myntra is an Indian fashion and lifestyle e-commerce platform and part of the Flipkart group. Its technology and analytics teams work on search, personalisation, size recommendations, pricing, supply chain and marketing. It hires for software engineers, SDETs, business analysts, data analysts and data scientists. Fashion-specific data such as sizing matters a lot.
For Data Analytics roles, this Myntra practice set focuses most on SQL (6), Python for Analysis (6), Power BI (6), Statistics and Case Studies (5). It has 4 easy, 29 medium, 7 hard questions, so you can start easy and work up to the harder ones.
- Company type
- E-commerce & consumer tech
- Headquarters
- Bengaluru, India
- Main India offices
- Bengaluru
Myntra Data Analytics interview process
- Online assessment (coding or SQL)
- Technical or machine coding round
- Problem solving or case round
- Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
What does CASE WHEN do?
Show answer
It adds conditional logic inside queries, for example bucketing ages or flagging values, returning different results per condition.
03
Your pandas script shows SettingWithCopyWarning and a new column does not update. What is happening and how do you fix it?
Show answer
It warns that you may be modifying a temporary copy of a slice, so the change might not reach the original DataFrame, for example df[df.sales > 0]['flag'] = 1. Avoid it by using a single loc call, df.loc[df.sales > 0, 'flag'] = 1, or by calling .copy() when you intentionally create a new subset. Newer pandas versions with copy-on-write make behaviour more predictable, but clear loc assignments remain best practice.
04
Estimate how many cups of tea are sold in a city of 1 crore people in a day.
Show answer
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
05
Explain INDEX and MATCH together.
Show answer
MATCH finds the position of a value in a range and INDEX returns the item at that position, giving a flexible lookup that works left or right and survives column insertions.
06
What is a confidence interval?
Show answer
A confidence interval gives a range of plausible values for a population parameter based on a sample. A 95 percent interval for average order value of 820 to 880 rupees means that if we repeated the sampling many times, about 95 percent of such intervals would contain the true average. Wider intervals mean more uncertainty; larger samples narrow them. Reporting the interval is more honest than reporting one number.
07
On day 4 of a planned 14-day test, the product manager wants to stop because the dashboard shows treatment winning. What do you say?
Show answer
I would explain that early results swing a lot and stopping on a good day inflates false positives, and that four days miss weekly patterns like weekend shopping. I would show how the lift has moved day by day and agree to the planned end date. If speed really matters, we can agree a sequential testing rule for future tests, but changing the rule mid-test after seeing data is not valid.
08
Write a DAX measure for year-over-year sales growth percentage.
Show answer
Sales LY =
CALCULATE([Total Sales], SAMEPERIODLASTYEAR('Date'[Date]))
Sales YoY % =
VAR cur = [Total Sales]
VAR prev = [Sales LY]
RETURN
DIVIDE(cur - prev, prev)
-- needs a marked date table; format the measure as a percentage
09
Your manager wants a visual that always shows the top 5 products for whatever region is selected in a slicer. How do you build it?
Show answer
The simplest way is a visual-level Top N filter on Product, by the Total Sales measure, set to 5; it recalculates for every slicer selection. If the number must be user selectable, create a numeric what-if parameter and a ranking measure using RANKX over ALLSELECTED(Product), then filter the visual where rank is less than or equal to the parameter. I would also add an Others row if the business wants totals to still match.
10
Write Python code to extract UTM source, medium and campaign from a column of landing page URLs.
Show answer
import pandas as pd
from urllib.parse import urlparse, parse_qs
def get_utms(url):
params = parse_qs(urlparse(str(url)).query)
return pd.Series({
'source': params.get('utm_source', ['(direct)'])[0].lower(),
'medium': params.get('utm_medium', ['(none)'])[0].lower(),
'campaign': params.get('utm_campaign', ['(not set)'])[0].lower(),
})
df[['source', 'medium', 'campaign']] = df['landing_url'].apply(get_utms)
11
Organic search traffic fell 40% overnight. How do you investigate?
Show answer
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
12
Your Sales table has OrderDate and ShipDate. How do you analyse sales by ship date when only OrderDate is the active relationship?
Show answer
-- create an inactive relationship Sales[ShipDate] -> 'Date'[Date] in the model
Sales by Ship Date =
CALCULATE(
[Total Sales],
USERELATIONSHIP(Sales[ShipDate], 'Date'[Date])
)
-- the measure uses the inactive relationship only inside this CALCULATE
-- the alternative is a second role-playing date table
13
How do you calculate a simple customer lifetime value?
Show answer
A simple formula is CLV = average order value x purchase frequency per year x gross margin x expected lifetime in years. Example: 800 rupees x 6 orders x 30 percent margin x 3 years = 4,320 rupees. Expected lifetime can be estimated as 1 divided by annual churn rate. A better version discounts future value and calculates CLV by cohort or segment, because averages hide very different customer groups.
14
What is the difference between Goal Seek and Solver?
Show answer
Goal Seek changes one input to hit a target output; Solver optimises with several variables and constraints, for example maximising profit under budget limits.
15
How would you automate a weekly report with Python?
Show answer
Pull data with SQL or APIs, clean and aggregate with Pandas, build charts and an Excel or PDF output, and schedule it with cron or Airflow with logging and alerts.
16
Tell me about a time a dashboard you built changed a business decision.
Show answer
Situation: our operations team assumed late deliveries were evenly spread. Task: I built a delivery performance dashboard. Action: I added a breakdown by courier partner and pin code cluster with a weekly trend, and reviewed it with the operations manager. Result: it showed one partner caused 60 percent of delays in two zones. The company shifted those zones to another partner, and on-time delivery rose from 84 to 92 percent in two months.
17
How do you change data types and parse dates?
Show answer
df['date'] = pd.to_datetime(df['date']); df['amount'] = pd.to_numeric(df['amount'], errors='coerce'); astype('category') for repeated strings.
18
Write SQL to generate a simple date dimension for 2026.
Show answer
-- PostgreSQL syntax
CREATE TABLE dim_date AS
SELECT
TO_CHAR(d, 'YYYYMMDD')::INT AS date_key,
d::DATE AS full_date,
EXTRACT(YEAR FROM d) AS year,
EXTRACT(QUARTER FROM d) AS quarter,
EXTRACT(MONTH FROM d) AS month_no,
TO_CHAR(d, 'Mon') AS month_name,
TO_CHAR(d, 'Dy') AS weekday,
EXTRACT(ISODOW FROM d) IN (6, 7) AS is_weekend
FROM GENERATE_SERIES(DATE '2026-01-01', DATE '2026-12-31', INTERVAL '1 day') AS d;
19
An inner join between orders and customers returns fewer orders than the orders table has. What could be wrong?
Show answer
Orphan records are child rows whose foreign key has no matching parent, such as orders with a customer_id that does not exist in the customer table. They break joins silently: an inner join drops them and totals fall short. Find them with a LEFT JOIN from the child to the parent and filter where the parent key IS NULL. Then fix the source, add an Unknown member, or report them separately.
20
Write SQL to find the number of days between each customer's consecutive orders and their average gap.
Show answer
WITH gaps AS (
SELECT customer_id,
order_date,
order_date - LAG(order_date) OVER (
PARTITION BY customer_id ORDER BY order_date) AS days_since_prev
FROM orders
)
SELECT customer_id,
COUNT(*) AS orders,
AVG(days_since_prev) AS avg_gap_days
FROM gaps
GROUP BY customer_id
HAVING COUNT(*) > 1;
-- order_date is DATE (PostgreSQL); use DATEDIFF in other databases
21
What are field parameters in Power BI?
Show answer
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
22
Write a Tableau table calculation for year-over-year sales growth.
Show answer
// with YEAR(Order Date) in the view, Compute Using: Table (across)
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1))
/ ABS(LOOKUP(ZN(SUM([Sales])), -1))
// the built-in Quick Table Calculation 'Year over Year Growth' generates similar logic
// format the field as a percentage
23
What is data cleaning and why does it take so much of an analyst's time?
Show answer
Data cleaning means fixing or removing incorrect, incomplete, duplicated or inconsistent data so analysis is trustworthy. Real data comes from many systems and manual entry, so you meet typos, mixed formats, missing values and duplicates. Analysts often spend half or more of a project on it because every later chart and model depends on it. Wrong inputs give confident but wrong answers, which is worse than no answer.
24
A measure shows correct values per row in a table visual, but the grand total looks wrong. Why, and how do you fix it?
Show answer
Totals in DAX are not the sum of visible rows; the measure is recalculated in the total's filter context. A measure like a distinct count, a ratio or an IF condition can give a different result at total level. If the business wants the sum of row values, iterate explicitly, for example SUMX ( VALUES ( Product[Name] ), [My Measure] ). Confirm with stakeholders which total is correct before changing it.
25
Write SQL for a running total of sales by date.
Show answer
SELECT order_date,
SUM(amount) OVER (ORDER BY order_date) AS running_total
FROM sales;
26
An A/B test is flat overall but significant for mobile users in Tier 2 cities. Do you ship it only for that segment?
Show answer
Not straight away. Slicing results into many segments makes a chance finding likely, so this is a hypothesis, not a conclusion. Check whether the segment was planned in advance, how many segments were examined, and whether there is a plausible reason, such as slower networks benefiting from a lighter page. If it still looks promising, run a follow-up test targeted at that segment with enough sample to confirm it.
27
Write Python code to fit a simple linear regression of sales on ad spend.
Show answer
import pandas as pd
import statsmodels.formula.api as smf
df = pd.read_csv('marketing.csv')
model = smf.ols('sales ~ ad_spend', data=df).fit()
print(model.summary()) # coefficients, p-values, R-squared
# predicted sales for a spend of 50,000
print(model.predict(pd.DataFrame({'ad_spend': [50000]})))
28
Write pandas code to compute month-on-month percentage change and cumulative sales for each store.
Show answer
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
29
A stakeholder says the Power BI report shows different revenue than their Excel file. How do you troubleshoot?
Show answer
I would first agree on definitions: gross or net, including tax or returns, order date or invoice date. Then compare one small slice, such as one month and one region, in both. I check filters on the report and page, relationships that drop unmatched rows, refresh time versus the Excel extract time, and duplicate rows. Once the gap is explained, I document the definition in the report so the question does not repeat.
30
How would you find customers who never placed an order?
Show answer
LEFT JOIN customers to orders and keep rows where order_id IS NULL, or use NOT EXISTS, which also handles NULLs safely.
31
What is the LET function in Excel and why would you use it?
Show answer
LET assigns names to intermediate results inside a single formula, for example =LET(sales, SUM(D2:D100), cost, SUM(E2:E100), (sales-cost)/sales). It makes long formulas readable and faster, because a repeated sub-calculation is evaluated once instead of several times. It is especially useful with dynamic arrays, where the same FILTER result might otherwise be recalculated three or four times in one nested formula.
32
What are parameters in Tableau and how do you use them?
Show answer
A parameter is a single user-controlled value, such as a number, date or string, that is independent of the data. On its own it does nothing; you reference it in calculations, filters, reference lines or bins. Common uses are a Top N selector, a what-if growth rate, or a measure switcher where a CASE statement picks Sales, Profit or Quantity based on the parameter. Parameter actions let users set it by clicking a mark.
33
A campaign landing page gets heavy traffic but very few sign-ups. How would you diagnose it?
Show answer
Check traffic quality first: is the right audience coming, and does the ad message match the page? Then look at page behaviour by device: load time, scroll depth, form start versus form submit, and which form field users abandon. Session recordings or heatmaps help. Test the form on mobile for errors. Common fixes are faster pages, fewer form fields, a clear call to action above the fold and matching the ad's offer.
34
How do you handle large CSV files that do not fit in memory in Python?
Show answer
Read in chunks with chunksize, load only needed columns, set efficient dtypes or switch to Dask, Polars or a database.
35
Your Top 10 products filter in Tableau ignores the category filter. Why, and how do you fix it?
Show answer
A context filter is applied before other dimension filters and before FIXED LOD calculations, creating a smaller dataset the rest of the view works on. Use it when a Top N filter should operate within a selected category, for example top 10 products within Electronics, or when a FIXED LOD must respect a filter. Add a filter to context from its right-click menu; it turns grey. Too many context filters can hurt performance.
36
What is a p-value, explained simply?
Show answer
A p-value is the probability of seeing a result at least as extreme as yours if the null hypothesis, usually no effect, were true. A p-value of 0.03 means that if the new page truly made no difference, results this strong would appear only about 3 percent of the time by chance. Below a chosen threshold such as 0.05 we reject the null. It is not the probability that the hypothesis is true.
37
What is the logical order in which SQL executes a SELECT query?
Show answer
Although written as SELECT first, a query is logically processed as FROM and JOIN, then WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY and finally LIMIT. This explains common errors: you cannot use a SELECT alias in WHERE because SELECT runs later, but you can use it in ORDER BY. It also explains why aggregates belong in HAVING, not WHERE, and why window functions cannot be filtered in the same query level.
38
An A/B test shows a 2% lift with p = 0.04, but it only ran for 3 days. Would you ship it?
Show answer
Not yet. Three days misses weekly patterns, and early results are often inflated by novelty effects or by stopping once the p-value looked good. I would check whether the planned sample size was reached, whether the split was balanced, and how guardrail metrics behaved. I would recommend running to the pre-planned duration, at least one or two full weeks, and then deciding, considering the cost of being wrong.
39
What is an A/B test and why is random assignment so important?
Show answer
An A/B test is a controlled experiment where users are split into a control group that sees the current version and a treatment group that sees a change, and you compare a metric such as conversion. Random assignment makes the groups similar in every way except the change, including things you cannot measure. That is why a difference in results can be attributed to the change itself, not to user mix or seasonality.
40
How do you combine text from several cells with a separator in Excel?
Show answer
-- join first and last name with a space
=TEXTJOIN(" ", TRUE, A2, B2)
-- all products of one order in a single cell, comma separated
=TEXTJOIN(", ", TRUE, FILTER(C2:C500, A2:A500=F2))
-- older method
=A2 & " " & B2
-- TRUE in TEXTJOIN skips empty cells, which CONCAT and & do not
Tips to crack the Myntra Data Analytics interview
- Prepare cases on fashion e-commerce issues like returns due to size, discount depth or End of Reason Sale planning.
- Practise SQL and Python for analytics roles, including cohort and funnel analysis on order data.
- Read about Myntra's use of data science in size recommendations and visual search.
- Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
- Practise the hard questions on this page aloud; Myntra interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Myntra Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (coding or SQL); Technical or machine coding round; Problem solving or case round; Hiring manager round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Myntra Data Analytics interview?
This practice set leans on SQL (6), Python for Analysis (6), Power BI (6), Statistics and Case Studies (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Myntra interviewers ask you to walk through real work.
Are these the exact questions asked at Myntra?
No. These are curated practice questions that match the skills and difficulty usually tested in e-commerce & consumer tech interviews like Myntra's. They are not a leaked or verified list of past questions.
Where does Myntra hire Data Analytics professionals in India?
Myntra has large teams in Bengaluru. Openings and locations change often, so check the Myntra careers page and filter by city and experience.
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