Interview experience
BigBasket interview experiences covering Online Assessment, Technical Interview 1, Technical Interview 2, HR Interview. Round-wise notes, common questions, and what candidates report clears each stage.
BigBasket Interview Questions and Answers (2026)
BigBasket is India’s large online supermarket and a Tata Digital company (Tata Group). Founded in 2011, it runs full-catalog grocery, bbnow quick commerce, bbinstant, bbdaily, and B2B lines like bbmandi. Engineering work sits closest to inventory, warehouse, slotting, and order systems - not a generic “e-commerce clone” of fashion marketplaces.
Explore: Interview experience · HR interview questions · Overview · Placement papers · Preparation guide
Overview
BigBasket’s fresher SDE loop is a 3-4 stage process where DSA and SQL fundamentals are consistently framed around grocery/e-commerce scenarios - inventory, catalog, and order management - rather than abstract textbook prompts.
HR interview questions
BigBasket HR interview questions with sample answer frameworks - motivation, relocation, STAR stories, and culture-fit prompts.
BigBasket interview process at a glance
| Round | Duration | What they test |
|---|---|---|
| Online Assessment | ~60 min | DSA problems, SQL queries, API-design based questions |
| Technical Interview 1 | 45-60 min | DSA/coding, problem-solving |
| Technical Interview 2 | 45-60 min | Project deep-dive, hands-on frontend/backend experience |
| HR Interview | 20-30 min | Background, fit, role/location expectations |
BigBasket online assessment: quick answers
These answers are compiled from public candidate reports and prep guides, not from an official BigBasket notice, and drives differ. Recent public reports are few, so the instruction screen of your own test is the final word.
| Question | Answer |
|---|---|
| How many questions and how long? | About 60-90 minutes. A September 2024 AmbitionBox report describes 3 coding questions and 5 MCQs, all very simple, with one Fibonacci question. Campus SDE reports describe three coding problems; a 2016 campus test ran 60 minutes. |
| Does it have negative marking? | No public report mentions any. Coding questions are scored on test cases. |
| Is there a sectional cutoff? | Not reported. Candidates describe one shortlist across coding, SQL and MCQs. |
| How much is the cutoff? | Not published. Eligibility in candidate reports ranges from 6.5+ CGPA or 65% to above 7 CGPA (2016 campus), with no active backlogs. |
| Which platform is it on? | HackerEarth on campus drives, in the reports that name one. |
| Is there SQL in the test? | Often. Intern and campus OAs include join and aggregate SQL alongside coding, and some add API or JWT questions. |
| How long does the whole process take? | Usually under 2 weeks, according to the September 2024 report and AmbitionBox’s summary. |
BigBasket interview questions by role
These topics come from AmbitionBox and Code360 interview reports. They show the level and style, not a fixed list.
| Role | Reported questions and topics |
|---|---|
| SDE-1 (campus) | Three coding problems (min-cost matrix path, Trie word matching, greedy puzzles); data structures, DBMS, OS and networks; deep questions on 2-4 projects. |
| SDE intern | LRU cache implementation, join and aggregate SQL, then a project deep dive. |
| Software developer (lateral) | A 1-hour video coding round (2018 SDE-2); MongoDB vs SQL, ACID, HTTP semantics, Node.js threading, Next.js CSR vs SSR and JWT login flow, depending on your stack. |
| MIS / operations executive | A single verbal round on Excel: basic functions, VLOOKUP and pivot tables (August 2024). |
| Operations controller | Self-introduction and experience in HR; inventory reconciliation, team handling, vehicle management and how you would run last-mile operations (January 2025). |
Asked across every role: why BigBasket, your experience in brief, and how you would handle an inventory or delivery problem.
Online Assessment
A roughly 60-90 minute screening test on HackerEarth (campus) or similar platforms, mixing DSA, SQL, and API-design questions - the steepest filter before human interviews. Campus SDE reports describe three coding problems (matrix DP/min-cost path, Trie word matching, greedy digit/slot puzzles); intern OAs add explicit LRU Cache implementations plus join/aggregate SQL.
Common questions
- Sliding-window and two-pointer array/string problems
- Design or reason about an LRU cache
- SQL joins and aggregation queries (e.g. top-selling products by category)
- Basic API-design/authentication concepts (JWT, REST conventions)
- Matrix traversal / min-cost path (DP or recursion)
- Trie-based dictionary or spell-check style prompts
Technical Interview 1
A DSA-focused round with a tech panel member, frequently framing standard algorithmic problems around BigBasket’s actual domain - inventory, catalog, and order flows.
Common questions
- Sliding-window or two-pointer problems reframed as an inventory/catalog scenario
- Design a data structure for order-tracking or cart management
- Time/space complexity trade-offs and edge cases on your solution
Technical Interview 2
Shifts from algorithms to a hands-on project deep-dive - expect detailed questions on your actual frontend/backend work rather than new coding problems. Engineering-manager panels often probe MongoDB vs SQL, ACID, HTTP semantics, Node.js threading, and your favourite sort (merge vs quick) even when the role is “full stack.”
Common questions
- Walk through your most complex project’s architecture and your specific contribution
- Frontend or backend implementation details relevant to your resume stack
- What would you change or rebuild if you did the project again?
- Next.js CSR vs SSR, JWT storage, and login → token → protected-route flow if on your resume
Round-by-round narratives are on the BigBasket interview experience page.
HR Interview
A closing 20-30 minute conversation on background, motivation, and fit - often includes a lightweight system-design sketch relevant to grocery delivery.
Common questions
- Tell me about yourself, and why BigBasket?
- Walk through the design of a feature you’d build for a grocery-delivery or inventory system
- Tell me about a time you optimized something for scale or performance under real-world constraints
- Location and role expectations
Sample answer frameworks for each of these are on the BigBasket HR interview questions page.
BigBasket tech hiring vs operations hiring
BigBasket hires engineers and operations staff through very different processes. Tech roles (SDE, software developer, intern) run an online test, one or two technical interviews and HR, as covered on this page.
Operations roles in warehouses, dark stores and last-mile delivery (operations controller, shift controller, MIS executive) usually skip the online coding test. Reports describe an HR round plus a practical round on Excel, inventory reconciliation, manpower and shift planning, or last-mile operations. If your role is in operations, prepare metrics and process examples rather than DSA.
Common technical interview questions and answers
Q: Find the maximum sum of any contiguous subarray of size k.
Use a fixed-size sliding window rather than recomputing each sum. Add the first k elements to get an initial window sum, then slide: for each new index i from k onward, add arr[i] and subtract arr[i - k], tracking the maximum seen. That is O(n) time and O(1) space, versus the O(n times k) brute force that re-adds every window from scratch. The variable-size version of the same pattern - grow the right edge, shrink the left while a constraint is violated - is what solves problems like the smallest subarray whose sum is at least a target, and interviewers usually ask you to extend to it. Note that the shrinking variant only works when all elements are non-negative, since with negatives the window sum is not monotonic and you need prefix sums with a hash map instead.
Q: Given a sorted array, find a pair that sums to a target.
Place one pointer at index 0 and another at index n minus 1. If the pair’s sum equals the target you are done; if it is smaller than the target, move the left pointer right to increase the sum; if it is larger, move the right pointer left. Each pointer moves at most n steps, so the whole scan is O(n) time and O(1) space, and it works because sortedness makes the sum monotonic in each pointer’s direction. If the input is unsorted, a hash set storing complements gives O(n) time with O(n) space and is usually preferred - sorting first would cost O(n log n). Interviewers commonly follow up with three-sum, which fixes one element and runs this two-pointer scan inside a loop for O(n squared).
Q: How do you implement an LRU cache with O(1) get and put?
Combine a hash map with a doubly linked list. The hash map maps a key to the list node holding it, giving O(1) lookup; the doubly linked list keeps entries in recency order, with most recently used at the head and least recently used at the tail. On get, look up the node and move it to the head. On put, either update and move the existing node to the head, or insert a new node at the head and, if capacity is exceeded, remove the tail node and delete its key from the map. The list must be doubly linked because eviction and reordering require unlinking a node in O(1) given only that node, which a singly linked list cannot do. In Java, LinkedHashMap with accessOrder set to true and an overridden removeEldestEntry gives the same behaviour out of the box.
Q: Write a SQL query for the top three best-selling products in each category.
Use a window function to rank within each partition: SELECT category, product_name, total_sold FROM (SELECT p.category, p.product_name, SUM(o.quantity) AS total_sold, ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(o.quantity) DESC) AS rn FROM order_items o JOIN products p ON o.product_id = p.id GROUP BY p.category, p.product_name) t WHERE rn <= 3; The window function must live in a subquery or CTE because you cannot filter on a window alias in the same SELECT’s WHERE clause - windows are evaluated after WHERE and GROUP BY. Choose ROW_NUMBER when you want exactly three rows regardless of ties, RANK when ties should occupy the same position and skip numbers, and DENSE_RANK when ties share a position without gaps.
Q: Explain JWT authentication and how it compares to server-side sessions.
A JSON Web Token is three base64url-encoded parts - header, payload, and signature - joined by dots, where the signature is computed by the server over the first two using a secret (HMAC) or a private key (RSA/ECDSA). Because the token is self-contained and signed, any service can verify it without a database lookup, which makes it well suited to stateless, horizontally scaled microservices. The catch is revocation: a session can be deleted server-side instantly, whereas a JWT stays valid until it expires, so the standard pattern is a short-lived access token of a few minutes plus a long-lived refresh token that is stored server-side and can be revoked. Two things candidates get wrong: the payload is only encoded, not encrypted, so never put secrets in it, and the server must reject a token whose header claims the none algorithm.
Q: Which HTTP methods are idempotent, and why does it matter for an order API?
GET, PUT, DELETE, HEAD and OPTIONS are idempotent - making the same call repeatedly leaves the server in the same state as making it once - while POST and PATCH generally are not. GET must also be safe, meaning it causes no state change at all, which is why a checkout action must never be a GET. This matters directly for orders: if a mobile client times out after sending a POST to place an order, a blind retry could create a duplicate order, so the standard fix is to have the client generate an idempotency key and the server store the result against that key, returning the original response on retry. The right status codes complete the picture - 201 with a Location header on creation, 409 on a conflicting state such as a duplicate cart merge, 422 for a semantically invalid payload, and 429 when the client is rate limited.
Q: How would you stop overselling the last unit of stock during a flash sale?
The failure mode is a read-then-write race: two requests both read stock as 1 and both decrement. The simplest correct fix is to make the check and the decrement one atomic statement - UPDATE inventory SET stock = stock - 1 WHERE sku_id = ? AND stock > 0; - and treat zero affected rows as sold out, since the database applies row locks and serialises the conflicting updates. Optimistic locking is the alternative: keep a version column, include it in the WHERE clause, and retry on failure, which suits low-contention paths but produces heavy retry churn on a hot SKU. At higher scale you move the counter into Redis and use an atomic DECR or a small Lua script so the decrement and the zero check are one operation, then reconcile to the database asynchronously. Reservations should also carry a TTL so abandoned carts release stock automatically instead of holding it forever.
Q: A category listing page has become slow. How do you diagnose and fix it?
Start with EXPLAIN on the actual query to see whether the planner is doing a full table scan, and check whether the filter and sort columns are indexed - a composite index on (category_id, created_at DESC) serves both the filter and the ordering in one structure, whereas two separate single-column indexes usually will not. Watch for anti-patterns that silently disable an index, such as wrapping the indexed column in a function or leading a LIKE pattern with a wildcard. Deep pagination with a large OFFSET is the other common culprit, since the database still walks every skipped row; keyset pagination, filtering on the last seen sort key, keeps cost constant regardless of page depth. If the query is already optimal and the traffic is read-heavy, add a cache in front keyed by category and filter set, serve reads from a replica, and for genuine text search move to a dedicated inverted-index engine rather than pushing LIKE queries at the primary database.
Q: Can an array be split into subsequences of k consecutive increasing integers?
Count frequencies in a hash map, then repeatedly peel valid chains: for each starting value x, if count[x] > 0, try to build a subsequence x, x+1, …, x+k-1 by decrementing each frequency; if any link is missing, return false. After processing all keys, every frequency should be zero. Sorting keys first makes the greedy peel deterministic. Time is O(n log n) with sorting or O(n) if you bucket by value. This pattern showed up in a reported campus Technical 1 round alongside live dry-runs in an IDE.
Q: How would you design delivery slot booking for a grocery app?
Separate slot inventory (capacity per dark store and time band) from order checkout. At browse time, show slots with cached availability; at checkout, reserve a slot with an idempotent POST keyed by cart id, decrement capacity in one atomic update (WHERE capacity > 0), and release on payment timeout via TTL or a sweeper job. Peak events need queueing or soft oversubscription with ops alerts rather than silent double booking. Tie the story to BigBasket-style constraints: perishables, cut-off times, and substitution after pick.
Frequently asked questions about BigBasket interviews
Does the BigBasket online assessment have negative marking?
No public report mentions negative marking. The BigBasket SDE online test is mainly coding - a September 2024 report describes 3 coding questions and 5 MCQs, all simple - and campus tests are described as coding problems scored on test cases.
What is the cutoff for the BigBasket online assessment?
Not published; shortlists are relative to the batch. Campus eligibility in candidate reports ranges from 6.5+ CGPA or 65% to above 7 CGPA (a 2016 campus drive), with no active backlogs. Your placement notice is the rule for your drive.
What is the BigBasket interview process for freshers?
BigBasket typically runs 3-4 rounds for SDE roles: 1. Online Assessment (about 60 minutes) - a mix of DSA problems, SQL queries, and API-design based questions, used as a screening round. 2. Technical Interview 1 (45-60 minutes) - DSA/coding and problem-solving with a tech panel member. 3. Technical Interview 2 (45-60 minutes) - more project-heavy, focused on hands-on frontend/backend experience. 4. HR Interview (20-30 minutes) - background, fit, and role/location expectations.
What questions are asked in BigBasket interviews?
Expect DSA patterns like sliding window, two pointers, and LRU caching, SQL joins and aggregations, and API/system-design basics like authentication and JWT. Because BigBasket runs a large-scale grocery e-commerce and delivery platform, interviewers often frame problems around inventory, catalog, or order-management scenarios rather than abstract examples.
How many rounds are there in the BigBasket interview?
Most BigBasket SDE drives run 3-4 rounds: an Online Assessment as a screening step, one or two Technical Interviews, and an HR round. Some experiences report the two technical rounds split as one algorithmic and one project/hands-on focused round.
What is the BigBasket technical interview like?
Technical Interview 1 (45-60 min) is DSA-focused - sliding window, two-pointer, and LRU-cache-style problems, often framed around a grocery/e-commerce scenario (inventory, catalog search) rather than an abstract prompt. Technical Interview 2 shifts to a project deep-dive and hands-on frontend/backend questions, testing whether you actually built and can defend what’s on your resume.
How should I prepare for BigBasket interviews?
Practice DSA patterns (sliding window, two pointers, LRU) and revise SQL joins/aggregations for the OA, and be ready to talk through the design of a feature relevant to e-commerce or grocery-tech - inventory, search, or order fulfillment are common framing devices. Know your projects well enough to discuss real trade-offs in the hands-on/project round.
Who owns BigBasket, and does that affect hiring?
BigBasket has been majority-owned by the Tata Group (via Tata Digital) since a 2021-2022 acquisition, and now operates as part of Tata’s broader digital/retail ecosystem alongside Tata Neu. This mainly affects backend integration priorities (Tata Neu tie-ins) rather than the fresher SDE interview format itself, which stays focused on DSA, SQL, and hands-on project depth.
How many rounds are in BigBasket SDE hiring?
Commonly OA → Tech 1 → Tech 2 → HR within a relatively short window for many campus drives.
What design prompts fit BigBasket?
Cart, slot booking, inventory reservation, search autocomplete - grounded in grocery constraints.
What CTC is candidate-reported?
SE-I ~₹6-10 LPA; campus SDE-1 often ₹9-14 LPA. Confirm the letter.
What interviewers are scoring at BigBasket
BigBasket scores grocery/quick-commerce reality (inventory, slots, perishability), DSA + SQL, and ownership of messy operational edge cases.
| Signal | What a strong answer looks like |
|---|---|
| OA | Medium DSA; SQL/API comfort when the paper mixes them |
| Tech | Orders, stock reservation, slot capacity discussed concretely |
| Stack honesty | Django/Java/Next claims you can debug |
| HR | Bengaluru hub, shift realities for some roles, Tata Digital context |
Behavioural themes to prepare
Why BigBasket/Tata Digital, customer wrong-order recovery, warehouse pressure, and integrity. Prep:
- Wrong item / late delivery proxy story - empathy + process fix
- Inventory conflict - accuracy over speed
- Why grocery tech - perishability and slotting as interesting problems
Frameworks: BigBasket HR interview questions.
How to prepare for BigBasket interviews
- Loop map: Online Assessment; Technical Interview 1; Technical Interview 2; HR Interview. Confirm timings from the drive mail.
- Technical depth: Drill the Q&A section until answers are spoken, not read. Know every resume project cold.
- Experiences: Study round-wise notes on the interview experience page - real prompts beat generic lists.
- HR: Three STAR stories + a specific “Why BigBasket?” on the HR page.
Who usually fits and who struggles
Good fit: You can write medium array/hash problems in 25 minutes, you have shipped an API that talks to MySQL/Postgres, and you can explain how an order should reserve stock. Grocery is messy - perishable SKUs, slot capacity, dark stores - and interviewers like people who enjoy that mess.
Harder path: You only grind LeetCode Easy, you freeze on GROUP BY, and your “full-stack project” is a tutorial clone you cannot debug. BigBasket is friendlier than FAANG on system-design theater, but they will still open JWT and rendering questions if your resume mentions Next.js.
A practical BigBasket prep plan
Use this as a default four-week loop before a BigBasket drive. Adjust to your invite timeline.
- Week 1: Language fluency: rewrite 10 old solutions in one language until you stop hunting syntax. Open the BigBasket previous-year sets on this site and mark which sections ate your time. Write the timing on a sticky note. That number is more useful than a 200-problem LeetCode streak you never timed.
- Week 2: Most BigBasket drives fail people on the first timed paper or OA. Do three timed sits this week. After each one, log every miss in a two-column note: topic and why (slow, wrong formula, bad edge case, panic). The next sit should only add problems from that log.
- Week 3: Technical interviews at BigBasket usually want a clean project story, one or two CS fundamentals, and (if the process has it) a behavioral or values round. Write STAR stories on paper, not in your head. Practise “Why BigBasket?” with one concrete product, lab, or business line - not a slogan.
- Week 4: Sleep, id photocopies, and a dry run of the oa platform if the invite names one. If you still have energy, redo only the questions you failed in week 2.
10-day crash version: Days 1-4 timed DSA, days 5-7 resume projects + CS fundamentals, days 8-9 mock interviews out loud, day 10 HR STAR stories on the HR page.
Compensation notes
Candidate-reported fresher/early-career packages for BigBasket often cite SE-I ~₹6-10 LPA; campus SDE-1 often ₹9-14 LPA. Treat forum numbers as ranges, not guarantees - confirm the written offer letter for your team and location.
Sources
Interview process details and sample questions on this site are editorially organized from public candidate reports and hiring write-ups, with round structure, prep notes, and company-specific framing added by our team. For round-by-round stories, see the BigBasket interview experience page. For OA papers and coding drills, use BigBasket on placementpapers.app. OA quick answers and role-wise questions draw on AmbitionBox BigBasket interview reviews for software developer, operations controller and MIS roles (2024-2025), Code360 BigBasket interview experiences and Hyring’s BigBasket interview guide.