Academic requirements
Floor: Often 6.5 CGPA / 65%
Degrees: B.E./B.Tech/M.Tech/MCA
Backlogs: None active at joining
Year: Final year / recent grad
BigBasket Placement Papers 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: Online Assessment · Download PDF · Placement Process · Interview Experience · Preparation Guide · Certifications & Career
BigBasket eligibility & CGPA (2026)
BigBasket does not publish one CGPA number that covers every India tech drive. Campus JDs, Tata Digital portals, and off-campus Software Engineer I posts disagree on floors. Treat the numbers below as candidate-reported as of August 2026 - your mailer wins.
Who this hub is for
- For: Students targeting SDE intern, SDE-1, or Software Engineer I on grocery/tech teams - mostly Bengaluru.
- Not the same track: Store ops, rider/partner, category, or pure sales roles. Those skip this OA story.
- Tata context: After the Tata Digital acquisition, some postings appear under Tata Digital / BigBasket branding. Confirm the legal employer on the offer.
Degree, CGPA, and “is 6.5 enough?”
6.5 CGPA / 65% is the floor students quote most often for campus tech shortlists. Some older off-campus chatter listed 10th/12th around 80% and graduation 75% - that is drive-specific, not a forever rule. A 6.6 with two passing OA solutions and a real backend project beats an 8.4 who cannot write a JOIN.
What actually gets you past resume screen: one project with APIs + a database, internships that mention production bugs, and a language you can defend (Python/Django or Java/Spring). A certificate pile with zero schema talk does not.
On-campus vs off-campus
| Route | What usually changes | What stays the same |
|---|---|---|
| Campus / intern | Batch OA, PPT, college CGPA list | DSA + project bar |
| Careers / Tata portal | Resume volume; sometimes Python+Django JD language | Still expect OA or assignment + interviews |
| Referral | Faster screen | OA/coding still decides |
| Intern → FTE | May skip one stage | You still defend the internship work |
Off-campus is not easier coding. It is more applicants and more “0-2 years Python Django” JDs that look intern-friendly until the SQL round.
Detailed eligibility criteria breakdown
| Item | Typical expectation (candidate-reported) | Reality check |
|---|---|---|
| CGPA / % | 6.5+ / ~65% on many campus lists | Some drives print higher 10/12 floors |
| Degree | B.E./B.Tech/M.Tech/MCA | Related computing degrees on many posts |
| Branch | CS, IT, ECE+ | Strong DSA helps adjacent branches |
| Year | Final year + recent grads (~1-2 years) | 2024-2026 batches appear on off-campus posts |
| Backlogs | No active backlogs at joining | Clear before paperwork |
| Languages | Java, Python; JS/TS if full-stack | Plus SQL |
| Location | Bengaluru default | Hyderabad/Mumbai more ops-heavy |
Skills that actually filter
Primary: Medium DSA + SQL
Secondary: Inventory, slots, orders
Stack signals: Python/Django or Java; REST, JWT
First filter: Timed OA
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.
Branch myth: ECE can clear if DSA + one backend project are real. Non-CS without coding practice should not treat Tata branding as a services-company aptitude mill.
Gaps and backlogs: Explain a one-year gap with skills, not a speech. Active backlogs at joining are a paperwork killer even if OA went well.
Selection feel by CGPA band
| CGPA range | Rough chances | Reality check |
|---|---|---|
| Below printed floor | Often filtered before OA | Hunt off-campus JDs with no hard cutoff |
| 6.5-6.9 | Eligible on many campus lists | SQL + DSA must carry you |
| 7.0-7.5 | Common shortlisted band | Projects start to matter |
| Above 7.5 | Helps resume volume | Still dies on a broken JOIN |
Bigbasket previous year paper PDFs
Download 2026 Bigbasket placement papers
2026 in detail
2025 placement papers
2024 placement papers
Previous year question papers
Comments & Suggestions
What Bigbasket previous year papers actually train
BigBasket does not publish an official forever paper set. “Previous year papers” on this site are pattern trainers for OA and early interviews - not leaked live tests.
Think of them as simulators for grocery-flavored DSA + SQL stamina. Themes repeat more than exact clones. If you can finish a 2025-style set under 90 minutes, a 2026 OA will feel familiar even when the LRU variant is new.
| Round (typical) | What to practice |
|---|---|
| Online assessment | Timed DSA + SQL joins/aggregations + light API MCQs |
| Technical 1 | Live coding (arrays/hashing/heaps) + project walkthrough |
| Technical 2 | Backend/full-stack + inventory/order talk |
| HR | Why BigBasket, Tata/grocery, Bengaluru joining |
Themes that keep repeating (2024-2026)
- 2024 cycles: Campus intern OAs already mixed DSA with SQL. LRU-cache style problems and join+aggregate queries showed up in public write-ups. Students who only practised HackerRank “30 days of code” without SQL lost the screen.
- 2025 loops: Technical 1 stayed practical - move-zeroes / two-pointers, then a long project grill (JWT, Next.js CSR vs SSR, auth middleware). Technical 2 went deeper on your architecture, not a whiteboard Instagram clone. Full-stack honesty beat buzzwords.
- 2026 prep expectation: Keep medium DSA warm, add SQL every week, and rehearse one grocery flow (slot booking or stock reservation). Off-campus SE-I posts still advertise Python/Django + REST + Git. Treat PDF packs as pattern catalogs.
How to use one paper set in a single sitting
- Timed: 60-90 minutes, one DSA medium + one SQL query + leftover API MCQs.
- Untimed review: rewrite the cleaner DSA; list three grocery edge cases (out-of-stock, duplicate order, slot overflow).
- Map each miss to a family (heap / two-pointer / JOIN) and pull cousins from coding questions.
- Spend 10 minutes explaining JWT or session auth out loud if your resume mentions it.
- Log one STAR story the session reminded you of (a production bug, a schema change).
Related problems to practice
| Theme in a set | What to drill next | Why BigBasket asks it |
|---|---|---|
| LRU / cache eviction | HashMap + DLL; TTL talk | Hot SKU / session caches |
| Move zeroes / two pointers | In-place arrays | Clean, testable coding bar |
| Top-K products | Heaps + hashing | Catalog ranking |
| Warehouse time windows | Interval merge | Slot capacity |
| Supplier dependency | Graphs / cycle detect | Procurement graphs |
| Order state machine | Enums + transitions | Checkout correctness |
| Join + GROUP BY | SQL aggregations | Ops dashboards, inventory |
2025 PDFs often drill “dedupe product IDs in window W.” Own the sliding-window + hash template, then drill a related problem for bbnow dark-store SKUs. That transfers; memorising story text does not.
BigBasket placement process, round by round
BigBasket hires freshers through campus placement cells, the careers portal / Tata Digital listings, referrals, and internship conversion. After the resume screen, the shape is product-grocery: timed OA, DSA, backend/project depth, then HR.
Application methods
Campus recruitment
PPT → resumes via the cell → on-campus or remote OA → technicals (sometimes same week) → HR.
Online applications
bigbasket.com / Tata careers. Expect a JD that names Django, REST, SQL, Git for SE-I.
Referrals
Higher resume visibility. You still write working code and explain projects.
1. Resume / eligibility screen (3-10 days)
They scan CGPA when the college printed a floor, internships, and whether you look like you have touched a database. Put one concrete backend line on the resume (“order service with Postgres, idempotent checkout”). Vague “interested in full stack” dies in volume.
2. Online Assessment - often 60-90 min
Public intern write-ups describe a mix, not three identical LeetCode hards:
| Component | Typical shape (reported) | Focus |
|---|---|---|
| DSA | 1-2 medium | LRU, arrays, hashing, heaps |
| SQL | 1 query | JOINs, GROUP BY, aggregation |
| API / backend | MCQ or short | REST status codes, auth basics |
| Duration | 60-90 min | Campus platform / HackerRank |
| Advance bar | Working DSA + SQL | Partial third problem is optional |
Time box that works: 5 minutes skim → 35-40 minutes DSA (including tests) → 20 minutes SQL → leftover API items. Do not spend 70 minutes polishing a heap if the JOIN is still blank.
Deeper notes: BigBasket Online Assessment.
OA sample themes
- Implement LRU cache (get/put, capacity)
- Move zeroes in-place / two-pointer cleanup
- Top-K SKUs by order count in a window
- SQL: revenue by category with a JOIN to orders
- REST: which status code for duplicate create vs missing resource
3. Technical interview 1 - ~45-60 min
Often a software engineer. Pattern from a 2025 campus intern story: one easy-medium DSA (move all zeroes to the end), then a long project discussion, then framework questions if your resume invited them (Next.js CSR vs SSR, JWT structure, login → token → protected routes).
You should:
- Clarify constraints and complexity target
- Code in-place solutions without thrashing the editor
- Explain your auth flow without reading a blog
- Admit what you did not build (do not claim “I designed the whole checkout”)
Silent staring after a failed test case is a common reject signal. Talk.
4. Technical interview 2 - ~45-60 min
More project-heavy: schema, API error handling, how you would stop overselling a perishable SKU, frontend state if you claimed full-stack. Fresher depth - they want ownership and honesty, not Kafka name-dropping.
Worked mini-outline - reserve stock then confirm order:
- Actors: app, inventory service, slot service, payment, warehouse.
- Happy path: check slot → reserve units → pay → confirm → create pick list.
- Failure: payment succeeds, warehouse is short → compensate, notify, do not double-charge.
- Consistency: short reservation TTL vs oversell; say what a fresher system can actually do.
- Idempotency: same checkout retry must not create two orders.
Worked mini-outline - delivery slot capacity:
- Dark store has N pickers and a max orders/slot.
- Booking is a counter with a cutoff time.
- Race: two users grab the last slot → one fails cleanly.
- Late cutoff: no new bookings; existing orders still pick.
- Observability: slot utilisation, cutoff breaches.
5. HR / offer - ~20-30 min
Why BigBasket (grocery + Tata, not “I like shopping apps”), Bengaluru joining, intern vs FTE conversion if applicable, CTC context. Ask one real question about bbnow vs full-catalog teams.
| # | Round | Duration | What they check |
|---|---|---|---|
| 1 | OA | 60-90 min | DSA + SQL accuracy |
| 2 | Technical 1 | 45-60 min | Code + projects + auth/SQL |
| 3 | Technical 2 | 45-60 min | Architecture honesty + grocery logic |
| 4 | HR | 20-30 min | Location, motivation, offer |
Some intern drives skip a separate HR and fold logistics into round 2. Trust the invite.
Timeline from application to offer
| Phase | Typical duration | Notes |
|---|---|---|
| Resume screen | 3-10 days | Faster with campus/referral |
| OA | 1 day | Results often 2-5 days |
| Interview loop | 1-2 weeks | Two technicals, sometimes same day |
| Offer | 1-5 days | Written letter wins |
| End to end | ~2-4 weeks | Off-campus can stretch |
Campus vs tata-portal practical differences
Campus intern loops can finish inside a placement week. Off-campus SE-I may add an assignment or a longer wait after OA. The technical bar does not drop because the JD says “0-2 years.” Django ORM questions replace “design Netflix” - still prepare to write SQL by hand.
Preparation resources for Bigbasket
Online assessment
OA format, timing, and question types.
Coding questions
DSA patterns for OA and technical rounds.
Aptitude / mock quiz
Quant, logic, and timed mock quiz.
Interview experiences
Candidate stories and round notes.
HR interview questions
Common HR questions, sample answers, STAR tips.
Week-by-week prep
Study plan and round-by-round strategy.
BigBasket preparation strategy that works
If a BigBasket invite is 3-8 weeks out, treat this hub as the map and the linked practice pages as the drills. Do not reread eligibility ten times. Check the JD once, then spend hours on the filter that actually rejects people.
Typical fresher CTC chatter for BigBasket sits around ₹6-10 LPA (candidate-reported - confirm the letter). Languages students mention most often: Java, Python, C, Go. Pick one and stay with it in OA and interviews.
Eligibility reminder (from student reports): Candidate-reported campus and off-campus posts commonly ask for about 6.5+ CGPA or 65% (some older drives listed higher 10th/12th floors), a B.E./B.Tech or related computing degree, no active backlogs at joining, and final-year or recent-graduate status. Python/Java plus SQL matter more than the exact CGPA once you clear the resume screen. Always verify the official careers page or campus JD.
What you are training for: Typical fresher tech loop: resume screen → online assessment (DSA + SQL/API mix, often 60-90 minutes) → Technical Interview 1 (DSA + projects) → Technical Interview 2 (backend/full-stack and grocery-systems talk) → HR. Campus intern drives sometimes stop at two technical rounds. End-to-end is often 2-4 weeks.
Short version of the prep split: Split time roughly 40% DSA (arrays, hashing, heaps, graphs), 25% timed OA mocks including SQL joins/aggregations, 20% backend + grocery domain (inventory, slots, orders), 15% projects and STAR stories. Practice LRU, two-pointers, and REST/JWT basics because those show up in real write-ups.
A four-week plan that actually gets used
Week 1 - pattern, not vibes. 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 - the steep filter. 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 - talk tracks. 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 - mocks and logistics. 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.
Where the hours should go
| Slice | Share of weekly hours | What “done” looks like |
|---|---|---|
| Timed papers / OA | ~40% | You finish a BigBasket-style sit without guessing the clock |
| Coding / role technical | ~30% | You can explain a solution out loud in your chosen language |
| Fundamentals / domain | ~20% | You can teach one OS/DBMS/networks (or domain) topic to a friend |
| Stories + HR | ~10% | Two STAR stories and a specific Why-this-company answer |
If you only have 10 days, keep the same ratios and cut volume, not the timed sits.
Practice pages to open this week
- OA / assessment notes
- aptitude mock quiz
- coding question bank
- interview experiences
- HR question bank
- week-by-week guide
- previous year papers
- 2026 paper page
Use this hub for the BigBasket story (eligibility, rounds, salary). Use those pages for timed reps and interview notes.
Common ways students waste a month
- Collecting 12 BigBasket PDFs and never sitting one under a timer.
- Switching languages every weekend because a senior used a different one.
- Memorising HR answers before they can finish the OA.
- Ignoring the company-specific quirk on this page (unique round, exam name, or role band) and preparing like a generic service-company drive.
If you do one thing after reading this: schedule three timed BigBasket papers on three different days, then interview prep on the leftovers.
If you are starting from a service-company prep base
A TCS/Infosys aptitude deck still helps the quant/logic slice of many BigBasket papers, but do not assume the same cutoff culture. Read the process section on this page once, then swap in BigBasket-specific coding or domain drills from the nested banks. Shared prep is fine; shared assumptions about rounds are not.
10-day crash version
Days 1-3: one timed BigBasket paper each day plus error log. Days 4-6: only the topics you missed, plus 4-6 coding problems in one language. Days 7-8: project + STAR + Why BigBasket. Day 9: one more full mock. Day 10: light review and sleep. This is worse than four weeks, but it beats rereading notes the night before.
What to carry into the interview room
Know your project at the level of what broke and what you changed, not only the tech list on the resume. For BigBasket, interviewers often poke the same hole the OA already tested - be ready to re-solve a warm question on a shared editor or whiteboard. If you used AI to draft code while practising, you still need to type it yourself on the day.
What BigBasket pays freshers (candidate-reported)
Figures below are candidate-reported / job-post chatter as of August 2026, not offer letters. Tata Digital bands move. Always read the written CTC split.
| Role band | Typical CTC (reported) | Notes |
|---|---|---|
| SDE intern (campus) | Stipend varies widely | Confirm monthly stipend + PPO language |
| Software Engineer I / backend (often Python) | ₹6-10 LPA | Common on off-campus Django-style posts |
| Campus / product SDE-1 | ₹9-14 LPA | Some reports cluster near ₹11 LPA in Bengaluru |
| Upper product chatter | ~₹12-16 LPA | Not the default SE-I off-campus number |
Older blogs quoting a flat ₹10-18 LPA for every fresher mix intern-conversion, SDE-1, and wishful Telegram. Use the role name on your JD.
Benefits students actually ask about
- Tata group medical / wellness (varies by entity)
- Employee grocery discounts (often mentioned, not a CTC substitute)
- Hybrid vs core Bengaluru office - ask the recruiter; do not assume remote
- Learning support is real on some teams, not a substitute for on-the-job warehouse complexity
How to read an offer: ask base, bonus, joining bonus, and probation. Intern PPOs should state conversion CTC, not only stipend. Relocation to Bengaluru is usually on you unless the letter says otherwise.
Recent BigBasket placement trends
Tata digital era
Branding may say BigBasket or Tata Digital. Employer name on the offer matters for benefits and bond language.
OA mix, not pure DSA
2025 intern write-ups still show SQL + API beside coding. Prep accordingly for 2026.
Bbnow vs supermarket
Quick-commerce and full-catalog teams share inventory DNA but different latency talk. Ask which org you are joining.
CTC honesty
SE-I Python posts are not Blinkit SDE-1 pay. Compare written offers, not brand prestige.
Net for 2026: do not wait for a “new pattern” rumor. Keep mixed OA mocks, one grocery sketch, and project-auth fluency. Process length is 2-4 weeks when campus is moving; off-campus waits are longer.
FAQs students ask about BigBasket placements
Process & papers
What is the BigBasket placement process?
Typical fresher tech path: Application / campus screen → Online Assessment (DSA + SQL/API, often 60-90 min) → Technical 1 (coding + projects) → Technical 2 (backend / grocery systems) → HR. Intern campus drives may stop after two technicals. Timeline often 2-4 weeks.
How many rounds are there in BigBasket interview?
Most stories report 3-4 stages after you are in process (OA + two tech + HR). Count the invite. Some same-day campus loops merge HR into the last technical.
Can I download BigBasket placement papers PDF?
Yes. Free BigBasket placement papers PDF and 2024-2026 style practice sets are on this site - pattern trainers, not leaked live papers. Start with the 2026 PDF. No registration.
What does the BigBasket OA look like?
Candidate-reported: 1-2 medium DSA problems plus SQL (joins/aggregation) and light REST/API items, 60-90 minutes. Aim to fully clear DSA and SQL. Platform is often HackerRank or the campus vendor.
Eligibility & offer
What is BigBasket eligibility criteria for freshers 2026?
Commonly reported: 6.5+ CGPA / 65%, B.E./B.Tech or related, no active backlogs at joining, final-year or recent graduate. Some older posts listed higher 10th/12th percentages. Your notification overrides this page.
What is BigBasket salary for freshers?
Candidate-reported (August 2026): SE-I / backend often ₹6-10 LPA; campus SDE-1 more often ₹9-14 LPA. Confirm the written split. Bengaluru is the usual base.
Do I have to join in Bengaluru?
Engineering stories are Bengaluru-centred. HR will check. Hyderabad/Mumbai show up more for non-core-tech. Be honest early.
Does BigBasket hire non-CS branches?
CS/IT/ECE dominate SDE/SE posts. Adjacent branches need visible DSA + a backend project. Do not assume all-branch eligibility.
Prep & BigBasket-specific
How to prepare for BigBasket placement?
About 40% DSA, 25% mixed OA + SQL, 20% grocery/backend, 15% projects/HR. Full plans: preparation guide.
What programming languages are allowed?
Java and Python are the usual pair; JavaScript/TypeScript if the role is full-stack. Off-campus SE-I often names Python + Django. SQL is expected either way.
Is SQL really asked?
Yes in multiple intern OA write-ups. Practise joins and aggregations until you can write them without an AI autocomplete.
BigBasket vs Blinkit vs Swiggy?
BigBasket = catalog grocery + Tata, warehouse/slots. Blinkit = 10-minute quick commerce (often higher SDE chatter). Swiggy = food + Instamart. DSA overlaps; domain stories should not.
Will they ask system design like FAANG?
Fresher depth: inventory reservation, slots, order states. They care more that you can reason than that you draw 12 microservices.
Any pro tip for BigBasket?
Weekly: two timed DSA mediums + one JOIN + one inventory/slot sketch. Know JWT/auth if it is on your resume. That combo matches 2025 intern loops better than grinding only graphs.
Where can I read real interview experiences?
Start with BigBasket interview experience on this site, then reputable public write-ups (for example campus intern notes on GeeksforGeeks). Use them as maps, not scripts.
BigBasket - recent candidate experiences
Campus SDE intern colour (2025)
A July 2025 GeeksforGeeks intern write-up described three stages: an on-campus OA with DSA (LRU cache) + SQL + API, Technical 1 with “move all zeroes to the end” plus a deep project grill (Next.js CSR vs SSR, JWT, auth flow), and Technical 2 as a project/architecture round. The author called the loop straightforward if DSA and full-stack fundamentals were already solid.
Takeaways that transfer:
- Practise LRU and in-place array problems until they are boring.
- Keep a SQL JOIN warm, not “I’ll learn it after OA.”
- If your resume says Next.js or JWT, you will be asked how, not whether you liked the tutorial.
- Round 2 is often “explain your own system,” not a surprise hard graph.
What selected profiles usually show
One backend-shaped project with a schema, medium DSA under time, and a Why BigBasket answer that mentions grocery logistics or Tata Digital - not “I use the app to buy bananas.” Empty culture lines get challenged.
After you clear OA - waiting-week plan
- Two timed mediums every other day - protect heaps and two-pointers.
- Rewrite project auth and schema on paper.
- Sketch reserve-stock → pay → pick-list with one failure.
- Two STAR stories with a metric.
- Sleep and Bengaluru answer.
Do not start a new DP course the night before Tech 1.
Experience story bank
| Story type | Prompt | Metric to include |
|---|---|---|
| Ownership | Feature you shipped | Latency, error rate, or users |
| Bug | Production or demo break | Time to detect → fix |
| Trade-off | SQL vs cache, SSR vs CSR | What you measured |
| Ambiguity | Unclear internship spec | How you scoped |
| Team | Disagreement on approach | What changed |
If a story has no number, it is not ready.
Mini OA drill to rehearse cold
Prompt: Implement LRU cache with get/put and capacity C. Then write SQL: top 5 categories by order count last 7 days.
- Clarify: O(1) expected? TTL?
- Code HashMap + doubly linked list (or language ordered-dict) with tests for capacity 1 and missing keys.
- SQL: join orders → products → categories, filter dates,
GROUP BY,ORDER BY COUNT DESC LIMIT 5. - State complexity before you leave the editor.
More narratives: BigBasket interview experience.