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Zomato’s fresher hiring is a coding-heavy, 4–5 stage loop where even entry-level technical rounds lean on system design and product-shaped problems - order tracking, ETAs, restaurant ranking - rather than generic textbook prompts. Campus drives for Eternal (Zomato’s listed parent; Zomato, Blinkit, and other consumer brands hire under this umbrella) often add CS fundamentals MCQs on the OA and, in recent SDE-1 reports, a dedicated low-level design (LLD) or machine-coding round before the hiring-manager conversation.
Interview experience
Zomato interview experiences covering Online Coding Assessment (OA), Technical Interviews (2-3 rounds), Managerial/Team Fit Interview, HR/Offer Discussion. Round-wise notes, common questions, and what candidates report clears each stage.
HR interview questions
Zomato HR interview questions with sample answer frameworks - motivation, relocation, STAR stories, and culture-fit prompts.
| Round | Duration | What they test |
|---|---|---|
| Online Coding Assessment (OA) | 90-120 min | 2-3 DSA problems, debugging questions |
| Technical Interviews (2-3 rounds) | 45-60 min each | DSA plus system design scoped to Zomato’s product (order tracking, ranking, delivery) |
| Managerial/Team Fit Interview | 45 min | Zomato values, cultural fit |
| HR/Offer Discussion | 20-30 min | Compensation, relocation, company fit |
These answers are compiled from public candidate reports and prep guides, not from an official Zomato or Eternal notice, and drives differ. The instruction screen of your own test is the final word.
| Question | Answer |
|---|---|
| How many questions and how long? | Usually 3 coding problems in about 90 minutes, graded easy, medium and hard (2023 on-campus and September 2025 Gurugram reports). A 2021 intern test had 2 coding problems in 60 minutes. Some drives add a short CS MCQ block. |
| Does it have negative marking? | No public report mentions any. The coding problems are scored on test cases; no report describes deductions on the MCQs either. |
| Is there a sectional cutoff? | Not reported. Where MCQs appear, candidates describe one combined shortlist, not a separate MCQ cutoff. |
| How much is the cutoff? | Not published, and shortlists can be tight: a 2024 Vellore campus report says only 10 students cleared an advanced DSA test, with GPA also used. Candidate-reported eligibility is 6.5+ CGPA or 65% with no active backlogs. |
| Which platform is it on? | Usually HackerRank or a similar proctored coding platform; many candidate reports do not name it, so go by your invite mail. |
| What topics come up? | Medium-hard DSA: data-stream parsing, binary search and Dijkstra (2023 campus), grid path counting (2021 intern), merging sorted arrays and LRU cache (2024 Bengaluru). |
| Do projects matter for the shortlist? | Yes, more than at most product companies. Candidates say Zomato values development work, and some campus drives shortlist on resume before the OA. |
These topics come from Glassdoor, Code360 and GfG interview experiences and analyst prep guides. They show the level and style, not a fixed list.
| Role | Reported questions and topics |
|---|---|
| SDE intern | 2-problem coding test, then DSA interviews plus OOP, DBMS, OS and networking basics such as SQL vs NoSQL and HTTP vs HTTPS. |
| SDE-1 (campus) | 3-problem OA; two one-hour technical rounds covering projects, WebSockets and authentication, a rate limiter for an API gateway backed by Redis, a strings problem best solved with a trie, concurrency, and SQL vs NoSQL; then a culture round (September 2025 Gurugram report). |
| SDE-1 / SDE-2 machine coding and design | In-memory rate limiter, Spotify-style shuffle, unbiased randint, order-status tracking and top-K restaurants by rating, with ETA and partner-allocation design for senior loops. |
| Business / product analyst (incl. Hyperpure) | Medium SQL questions and predicting join outputs; for data roles, up to 6 LeetCode medium-hard SQL questions; guesstimates such as weekly Zomato orders in Guwahati; cases on the Hyperpure supply chain or a drop in happy-delivery rate. |
Asked across every role: deep questions on two or three of your projects, why Zomato, and a time you took ownership of a customer problem.
A 90–120 minute test on HackerRank or similar, often mixing 15–20 CS MCQs (OS, networks, DBMS, OOP) with 2–3 DSA problems from medium to hard, plus occasional debugging segments. Solving problems with clean, edge-case-aware code - not only brute force - is the usual bar to advance; some Eternal campus OAs also include hash map + greedy + priority-queue/binary search combinations.
Common questions
2–3 rounds of 45–75 minutes each, mixing live DSA with design. Several 2024–2026 SDE-1 write-ups add a machine-coding / LLD round (e.g., in-memory rate limiter, Spotify-style shuffle, randint without bias) before or alongside high-level design. Design prompts are framed around Zomato’s product - predict delivery ETA, restaurant search, partner allocation - and scale up with seniority.
Common questions
Round-by-round narratives are on the Zomato interview experience page.
A pass/fail hiring-manager round (~45 minutes) - not a formality after strong coding. Interviewers map questions to Zomato’s stated values: customer focus, ownership, innovation, and bias for action in fast-moving ops. Expect STAR stories where you made the call, plus genuine questions about the team’s problems (Hyperpure, logistics, consumer app, ads - whichever unit you applied to).
Common questions
A short closing conversation on motivation, logistics, and compensation once the technical and managerial rounds are cleared.
Common questions
Sample answer frameworks for each of these are on the Zomato HR interview questions page.
Eternal Ltd is the listed parent company. Zomato (food delivery), Blinkit (quick commerce), Hyperpure (restaurant supplies) and District (going out) are its businesses, and campus invites may say Eternal, Zomato, Blinkit or “Zomato x Blinkit”.
The coding OA and DSA rounds look much the same across brands. The design and domain rounds differ: Zomato teams ask about restaurant search, order tracking and delivery ETAs, while Blinkit teams ask about dark-store inventory, 10-minute delivery and catalogue. This page covers Zomato food-delivery and Hyperpure roles; check which brand and team your offer letter names, because pay and location can differ.
Walk the array keeping two values: currentSum, the best sum of a subarray ending at this index, and maxSum, the best seen anywhere. At each element, currentSum becomes the maximum of the element alone and currentSum plus the element - that choice is whether to extend the previous subarray or restart here - and maxSum takes the maximum of itself and currentSum. It is O(n) time and O(1) space. The classic trap is an all-negative array: initialising maxSum to 0 wrongly returns 0, so initialise it to the first element instead. To return the indices, record a start whenever you restart and commit start and end whenever maxSum updates.
Push the head of each list into a min-heap keyed by node value, then repeatedly pop the smallest node, append it to the output, and push that node’s successor if it exists. With N total nodes across K lists, this is O(N log K) time and O(K) space for the heap. The alternative is divide and conquer: pair up the lists and merge them two at a time over log K rounds, which is also O(N log K) but O(1) extra space when merging linked lists in place. Merging them sequentially one by one is the naive answer at O(N times K), which interviewers expect you to reject out loud.
Do not sort everything at O(n log n) if K is small. Maintain a min-heap of size K: push each restaurant, and whenever the heap exceeds K, pop the smallest, so the heap always holds the current best K and its root is the cutoff. That is O(n log K) time and O(K) space, and it works on a stream where the full dataset never fits in memory. Quickselect gives O(n) average time when the data is already in memory and you can reorder it, but degrades to O(n squared) in the worst case. In production you would precompute this per city or per cuisine and cache it, since the ranking changes far more slowly than it is read.
For minimum coins, define dp[i] as the fewest coins summing to amount i, initialise dp[0] to 0 and everything else to infinity, then for each amount i and each coin c where c is at most i, set dp[i] to the minimum of dp[i] and dp[i - c] + 1. The answer is dp[amount], or -1 if it is still infinity. That is O(amount times number of coins) time and O(amount) space. The counting-ways variant needs the loops in the other order - coins on the outside, amounts on the inside - otherwise you count permutations rather than combinations. A greedy largest-coin-first approach is wrong for arbitrary denominations, for example coins of 1, 3, and 4 making 6.
Model the order as a finite state machine - placed, accepted, preparing, picked up, out for delivery, delivered - with an explicit table of allowed transitions, so an invalid jump is rejected rather than silently written. Keep the current state in a fast key-value store keyed by order ID for O(1) reads, and append every transition with a timestamp to an immutable event log, which gives you the full history for support and analytics. Push updates to the customer’s app over WebSocket or server-sent events with a polling fallback, and publish each transition to a queue so the delivery, notification, and analytics services react independently. Include the transition version in each write so a delayed out-of-order update cannot move the order backwards.
Scanning every restaurant and computing haversine distance is O(n) per query and will not hold up. Instead index locations by a spatial key: geohash encodes latitude and longitude into a string where a shared prefix means spatial proximity, so a lookup becomes a prefix range scan; Uber’s H3 hexagonal grid is the same idea with uniform cell adjacency. Query the user’s cell plus its neighbouring cells - necessary because a restaurant just across a cell boundary is still close - then compute exact distances only on that small candidate set and sort. Quadtrees are the alternative when density varies wildly, since they subdivide only where points are dense. Cache results per cell, as many users in one area issue nearly the same query.
Start with candidate generation, which narrows millions of restaurants to a few hundred using cheap signals: deliverable in the user’s area, currently open, and matching broad taste from collaborative filtering over past orders. Then a ranking model scores those candidates on richer features - the user’s cuisine and price history, restaurant rating and recent order volume, distance and predicted delivery time, and time of day, since breakfast and dinner intent differ sharply. Finally apply business rules: suppress restaurants that are out of stock or heavily delayed, and inject some exploration so new restaurants can accumulate signal. Serve precomputed embeddings from a vector store to keep latency low, and evaluate online with A/B tests on conversion, not just offline accuracy.
Dijkstra keeps a min-heap of nodes keyed by tentative distance from the source. It pops the closest unvisited node, finalises its distance, and relaxes each outgoing edge by checking whether going through this node improves a neighbour’s distance. With a binary heap that is O((V + E) log V), and it is correct only when all edge weights are non-negative. For real delivery ETAs it is a starting point, not the answer: edge weights change with live traffic, the estimate must also include restaurant preparation time, order queueing, and rider pickup, and A star with a geographic heuristic explores far fewer nodes than plain Dijkstra on a road network. Production systems typically feed these signals into a learned model rather than reporting raw path time.
Each client (user, restaurant, or API key) gets a bucket with capacity B tokens refilled at rate r per second. On request, if at least one token exists, decrement and allow; else reject or queue with backoff. Store bucket state in Redis with atomic Lua scripts or a central service for multi-instance consistency. Discuss burst tolerance vs fairness, per-route limits (search vs checkout), and observability when limits trip - Zomato interviewers often ask for edge cases (clock skew, thundering herd after refill).
Fisher–Yates shuffle permutes an array in O(n) with uniform probability. If you must stream or lack full memory, reservoir sampling or swapping with a cryptographically sound RNG may apply. Campus reports use this to test implementation care before a product follow-up (e.g., randomising restaurant collections without repeating the same outlet). State invariants: every permutation equally likely, O(1) extra space for the in-place variant.
Split prep time (restaurant queue, item complexity, historical kitchen SLA), rider assignment (distance, fleet availability), and route time (traffic, last-mile). Inputs: geohash/H3 cell, time of day, rain flags, order value, veg/non-veg kitchen lanes. Output: percentile band (p50/p90) rather than a single minute - set customer expectations and reduce support tickets. Mention feedback loops when actuals arrive late.
No public report mentions negative marking. The Zomato SDE OA is mainly a coding test - usually 3 DSA problems of easy, medium and hard difficulty in about 90 minutes - and the few drives that add CS MCQs have not been reported to deduct marks for wrong answers.
Not published; shortlists are relative to the batch and can be very tight. A 2024 Vellore campus report says only 10 students were shortlisted after an advanced DSA test and that GPA was also used. Candidate-reported eligibility is 6.5+ CGPA or 65% with no active backlogs.
Zomato’s fresher SDE loop typically runs 4-5 stages: 1. Online Coding Assessment (90-120 min) - 2-3 DSA problems plus debugging questions. 2. Technical Interview(s) - 2-3 rounds of 45-60 minutes each mixing DSA with system-design questions framed around Zomato’s actual domain (restaurant ranking, order tracking, delivery ETAs). 3. Managerial/team-fit round on Zomato’s stated values (customer focus, ownership, innovation). 4. HR/offer discussion on relocation and compensation. Total timeline is roughly 2-4 weeks for campus drives.
DSA problems span arrays, trees, graphs, dynamic programming, and heaps (classic examples: maximum-sum subarray, LRU cache design, merge k sorted lists, top-K elements). System-design questions are usually reframed around Zomato’s own problems - designing an order-tracking system, a restaurant recommendation/ranking engine, or a rate limiter - rather than generic textbook prompts. Behavioural rounds use Zomato’s stated values (customer focus, ownership, bias for action) and expect STAR-format answers.
Most fresher SDE drives run 4-5 stages: an online coding assessment, 2-3 technical interviews (DSA plus system design), a managerial/team-fit round, and a closing HR/offer discussion. Some drives compress the managerial round into the final technical interview instead of running it separately - confirm the exact structure from your placement cell or recruiter email.
Each technical round (45-60 minutes) mixes a DSA problem solved live on a shared screen with a system-design discussion scaled to the role level - SDE-1 candidates get lighter-weight design questions (e.g. a single feature like order-status updates), while SDE-2+ candidates get fuller system-design prompts (recommendation engines, distributed caching, scaling to millions of users). Interviewers weigh code quality, edge-case handling, and how clearly you explain trade-offs, not just whether the code runs.
Practice medium-to-hard DSA (arrays, graphs, DP, heaps) on a platform where you can talk through your approach out loud, since Zomato interviews are explicitly evaluated on communication as well as correctness. For system design, practice mapping generic patterns (rate limiters, recommendation systems, real-time tracking) onto food-delivery-shaped problems specifically. Prepare 2-3 STAR stories that map cleanly onto ownership, customer focus, and handling ambiguity - Zomato’s HR/managerial rounds ask for these by name.
Yes, more than most product companies at the same level. Even SDE-1 candidates are commonly asked to design or extend a slice of Zomato’s actual product - order tracking, restaurant search/ranking, delivery-partner allocation - rather than a generic “design X” prompt. You don’t need production-scale answers, but you should be able to reason about the specific constraints of a real-time, location-based marketplace.
Largely, yes - campus and off-campus drives increasingly sit under the Eternal umbrella (Zomato, Blinkit, and related consumer brands). OA and interview shapes can still say “Zomato x Blinkit” or Eternal on the invite. Prep for food-delivery and quick-commerce themes (catalogue, dark-store logistics, ETAs) and confirm which brand/team the offer letter names - compensation and location still vary by role.
No. Candidate reports treat it as pass/fail on ownership, customer focus, ambiguity handling, and whether you want marketplace problems. HR then covers Why Zomato, relocation (Gurugram/Bengaluru), and compensation. Skipping STAR prep after a strong OA is a common failure mode.
Use this as a default four-week loop before a Zomato drive. Adjust to your invite timeline.
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.
Candidate-reported fresher/early-career packages for Zomato often cite SDE-1 ~₹18-25 LPA total. Treat forum numbers as ranges, not guarantees - confirm the written offer letter for your team and location.
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 Zomato interview experience page. For OA papers and coding drills, use Zomato on placementpapers.app. OA quick answers and role-wise questions draw on Glassdoor Zomato and Hyperpure interview reviews (2023-2025), Code360 and GfG Zomato interview experiences, and Dataford’s Zomato product analyst guide.
