BigBasket Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About BigBasket hiring for Data Analytics roles
BigBasket is an Indian online grocery company, majority owned by the Tata Group, that delivers fruits, vegetables, staples and household products. It runs scheduled delivery and quick commerce services such as BB Now across many cities. Its Bengaluru technology and business teams hire software engineers, data analysts, data scientists, supply chain analysts and product managers.
For Data Analytics roles, this BigBasket practice set focuses most on Statistics and Case Studies (7), Power BI (6), Web & Marketing Analytics (5), Python for Analysis (4). It has 6 easy, 23 medium, 11 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
BigBasket Data Analytics interview process
Online coding or analytics assessment
Technical round 1
Technical round 2 (design or case)
Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
Write pandas code to find the top 2 products by revenue in each region.
Lead with the answer, use one simple chart per point, translate numbers into business impact (₹, %), avoid jargon and end with a clear recommendation and next step.
Each comparison at alpha 0.05 has a 5% false positive chance, so checking 20 metrics will probably flag one as significant by luck. This is the multiple comparisons problem. Handle it by choosing one primary metric in advance, treating others as exploratory, and adjusting p-values with Bonferroni or Benjamini-Hochberg when several comparisons matter. With many variants, compare each to control and adjust, or use a larger sample.
A ratio like conversion rate or average order value can move because the numerator or the denominator changed, so always show both components nearby. Ratios can look dramatic on small bases, such as 2 of 4 users. They also cannot be averaged across segments; you must divide total numerator by total denominator, otherwise small segments get too much weight. Counts are simpler but need context like trends or targets.
07
Write Python code for a simple difference-in-differences estimate of a store pilot.
Show ranges, not just a single number: a forecast line with a shaded prediction interval, or a statement like revenue will likely be between 48 and 55 crore, most likely 51. Explain the main assumptions and what would change the outcome, perhaps with best, base and worst scenarios. Use plain words such as likely or unlikely consistently. Hiding uncertainty makes a forecast look precise but leads to bad decisions when reality differs.
09
Tell me about a time you found a serious data quality issue.
Situation: while building a returns report I noticed one warehouse had zero returns for three months. Task: confirm whether it was real. Action: I compared with the source system and found its return codes had changed after a software update, so my filter missed them. I informed the data owner, updated the mapping and back-filled the history. Result: the corrected report showed that warehouse had the highest return rate, which led to a packaging review.
10
Write a Tableau calculated field for profit ratio and a profit category.
// Profit Ratio (aggregate first, then divide)
SUM([Profit]) / SUM([Sales])
// Profit Category
IF SUM([Profit]) < 0 THEN 'Loss'
ELSEIF [Profit Ratio] < 0.1 THEN 'Low margin'
ELSE 'Healthy'
END
// avoid AVG([Profit]/[Sales]): it averages row ratios and gives a wrong total
11
Tell me about a time you were at risk of missing a reporting deadline.
Situation: a month-end report was due at 10 am, and at 8 am the source data had not loaded. Task: deliver something reliable. Action: I informed my manager immediately, checked with the data team for an estimate, and prepared the report on the previous day's data, clearly labelled. Result: leadership got the report on time with a caveat, and I sent the final version by noon. We later added a data arrival alert.
12
An A/B test shows a 2% lift with p = 0.04, but it only ran for 3 days. Would you ship it?
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.
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
14
Write Python code to extract UTM source, medium and campaign from a column of landing page URLs.
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.
17
Your scheduled Python report failed silently and nobody noticed for a week. How do you make it reliable?
Add logging to a file or central log, wrap the main steps in try and except that send an alert by email or chat on failure, and exit with a non-zero code so the scheduler marks the job failed. Add data checks, such as row counts above a minimum and no missing dates, before sending output. Use a proper scheduler or orchestrator with retries, and include the data date in every report.
18
What do the match_mode and search_mode arguments of XLOOKUP control?
match_mode decides how a value is matched: 0 exact (default), -1 exact or next smaller, 1 exact or next larger, and 2 wildcard. search_mode decides direction: 1 searches first to last, -1 searches last to first, and 2 or -2 use binary search on sorted data. Searching last to first is a quick way to fetch the latest entry for a customer without sorting, and next-smaller matching handles tax slabs or commission bands.
19
A marketing campaign shows more sales. How do you prove it worked?
WITH touches AS (
SELECT t.user_id, t.channel, t.touch_ts,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts) AS first_rank,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts DESC) AS last_rank
FROM marketing_touches t
JOIN conversions c
ON c.user_id = t.user_id
AND t.touch_ts <= c.converted_ts
)
SELECT user_id,
MAX(CASE WHEN first_rank = 1 THEN channel END) AS first_touch,
MAX(CASE WHEN last_rank = 1 THEN channel END) AS last_touch
FROM touches
GROUP BY user_id;
A central fact table (sales, numeric events) linked to dimension tables (date, product, customer). It keeps Power BI models simple, fast and correct when filtering.
23
Write a DAX measure that counts new customers, meaning customers who bought in the selected period but never before it.
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
MCAR, missing completely at random, means missingness is unrelated to anything, like a random sensor glitch; dropping rows is mostly safe. MAR, missing at random, means missingness depends on other observed variables, for example older customers skipping the email field; impute using those variables. MNAR, missing not at random, means it depends on the missing value itself, like high earners hiding income; this biases results and needs careful handling or extra data.
Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.
27
Write SQL to find the number of days between each customer's consecutive orders and their average gap.
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
28
Write a Tableau table calculation for year-over-year sales growth.
// 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
29
What is the difference between a calculated field and a helper column in Excel?
A calculated field lives inside a PivotTable and works on summarised data; a helper column is a normal column of formulas that feeds the PivotTable or other formulas.
30
Write Python code to fit a simple linear regression of sales on ad spend.
Situation: a new analyst on my team kept getting review comments for inconsistent numbers. Task: help him become independent. Action: I paired with him on two reports, shared a checklist covering definitions, row-count checks and chart standards, and set a weekly 30-minute review where he presented his work. Result: within six weeks his reports passed review with minor edits, and the checklist became part of team onboarding.
33
Checkout conversion fell from 3.2% to 2.6% the week after an app release. How do you investigate?
First confirm it is real: check tracking events, data delays and whether the denominator changed. Then segment by app version, platform, device, payment method and traffic source. If the drop is concentrated in the new version or one payment option, it points to a bug or UX change. Look at funnel steps to find where users drop. Compare with seasonality and campaigns, then share findings with product and engineering quickly.
34
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
Keep two fact tables at their natural grains: fact_sales at order line and fact_target at region-month. Never spread targets onto order lines. Both share conformed dimensions at a common level, such as a month attribute in the date dimension and region in the geography dimension. In reports, compare them only at month and region or higher. In Power BI, relate targets to month-level or region-level tables, not to daily keys.
36
What is the difference between users, sessions and events in Google Analytics 4?
GA4 uses an event-based model: every interaction, such as page_view, scroll, add_to_cart or purchase, is an event with parameters. A session is a group of events from one user within a period of activity, ending after 30 minutes of inactivity by default. A user is identified by a device cookie, app instance or a logged-in user ID. One user can have many sessions, and each session many events.
37
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
38
Traffic suddenly doubled from one city, with near-zero engagement time and no conversions. What is happening and what do you do?
This is very likely bot or spam traffic, or a misfiring tag. Check the user agents, IP ranges, hostnames, screen resolutions and landing pages for that traffic. If it is bots, exclude it with filters or data segments in analytics and in your reporting layer, and inform the web team so they can block it. Then restate the affected dashboard numbers, because bots inflate sessions and pull down conversion rate.
39
Which KPIs would you include in an HR attrition dashboard?
Core KPIs: headcount, monthly and annualised attrition rate, voluntary versus involuntary split, and early attrition within the first 90 days or first year. Breakdowns by department, location, manager, tenure band, performance rating and salary band show where it concentrates. Add hiring metrics such as time to fill and offer acceptance rate, and exit reasons from exit interviews. Protect privacy by hiding very small groups.
Correlation shows the strength and direction of association; regression builds an equation to predict one variable from others.
Tips to crack the BigBasket Data Analytics interview
Practise DSA for engineering roles, and SQL plus Excel for analyst roles, as these are commonly tested in screening.
Think through grocery supply chain problems like demand forecasting, inventory freshness and delivery slot planning before case rounds.
Use the BigBasket app and note improvement ideas, which helps in product sense and why-BigBasket discussions.
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; BigBasket interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the BigBasket Data Analytics interview?
Candidates usually go through 4 stages: Online coding or analytics assessment; Technical round 1; Technical round 2 (design or case); 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 BigBasket Data Analytics interview?
This practice set leans on Statistics and Case Studies (7), Power BI (6), Web & Marketing Analytics (5), Python for Analysis (4). Revise those first, then practise explaining one or two of your own projects end to end, because most BigBasket interviewers ask you to walk through real work.
Are these the exact questions asked at BigBasket?
No. These are curated practice questions that match the skills and difficulty usually tested in e-commerce & consumer tech interviews like BigBasket's. They are not a leaked or verified list of past questions.
Where does BigBasket hire Data Analytics professionals in India?
BigBasket has large teams in Bengaluru. Openings and locations change often, so check the BigBasket careers page and filter by city and experience.