How Swiggy SDE-1 interviews usually run for campus and off-campus hiring, synthesized from public candidate write-ups. We reorganized SQL+DSA OAs, multi-round problem solving, machine coding / LLD, and hiring-manager patterns into a prep map - treat round counts as year- and seniority-dependent.
Round-wise process at a glance
Round
Typical format
What clears you
Online Assessment
HackerRank; ~90 min; SQL + 2-3 coding OR 3 DSA
Strong solve rate; SQL correctness
DSA / Problem Solving
45-60+ min; 2-4 medium problems
Optimal complexity + edge cases
Machine Coding / LLD
1-3 hours (experienced tracks)
Working code, OOP, naming, tests
Hiring Manager
20-60 min
Project depth + behavioural fit
HR
Package / logistics
Clarity and honesty
Campus eligibility in one 2021 note: CSE/ECE/EEE, CGPA 7+.
Candidate profile: CSE/ECE/EEE, CGPA 7+, HackerRank OA
Round 1 - Online Assessment (90 min)
1 medium SQL + 3 coding (easy / medium / hard)
One account: solved 3.5 questions; 11 shortlisted
Tip: Practise LeetCode SQL medium and most-liked DSA per difficulty
Round 2 - Technical DSA (~1 hour)
Search in row-wise and column-wise sorted matrix - O(m+n) time, O(1) space
Min stack with O(1) getMin
Balanced parentheses with tight space constraints
Valid parenthesis string (LeetCode-style) O(n) time O(1) space
Interviewers friendly; discuss edge cases together
9 advanced to final in that account
Result: Cleared
Round 3 - Managerial (~30 min)
If I spoke to your professor, two positives / two negatives - and what you did about negatives
Project deep dive: motivation, problems, tech stack
Backend vs Android interest; full-stack willingness
React vs vanilla JS; Node vs Express; why MongoDB
Ask questions at the end
Same account: 4 selected for SDE-1
Final result: Offer received
Takeaways
What decided it: Strong OA solve rate (3.5/4 in one account), then O(m+n)/O(1) matrix search, O(1) min-stack and parentheses, plus honest stack trade-offs in managerial.
Common fail: Weak SQL on the 90-minute OA; vague “Why MongoDB” without a project reason.
How to practise: Timed LeetCode SQL medium + matrix/min-stack drills; rehearse professor-perspective strengths/weaknesses.
Candidate profile: Experienced backend (Java/Spring or Node); Bangalore onsite/virtual mix
Round 1 - DSA
Two medium–hard problems framed with Swiggy-like wording (DP + binary search, string manipulation)
Pattern recognition matters more than memorizing problem IDs
Round 2 - LLD
Top-K restaurant discovery or similar ranking feature - entities, APIs, scaling hooks
Round 3–4 - Hiring managers
HLD: split one cart into multi-restaurant domain orders, sibling identification via parentOrderId, race handling
Second HM: architecture depth, scaling, decision-making under ambiguity
Timeline: multi-week process with feedback between HM rounds (candidate-reported)
Key takeaways: Senior loops emphasize distributed order models and ownership narrative over raw coding speed.
Pre-interview checklist (Swiggy)
Confirm OA platform (HackerRank, HackerEarth, or internal) and whether SQL is included.
Drill binary search variants (rotated array, single duplicate in sorted array) - frequently reported.
Prepare one LLD build: parking lot, notifications, or driver assignment with concurrency story.
Prepare one HLD narrative: coupons, inventory, or order assignment with APIs + schema.
Rehearse HM stories: incident ownership, trade-off under deadline, why Swiggy tied to logistics scale.
Align answers with Swiggy values language (consumer-first, bias for action) on the HR page.
Sample answers for high-frequency questions
Use these as frames, not scripts.
Technical
Q. Search in a row- and column-sorted matrix in O(m+n), O(1) space. A. Start at top-right (or bottom-left). If current > target move left; if < target move down. No extra matrix copy.
Q. Min stack in O(1). A. Dual stack (values + mins) or encode min in one stack. Push/pop/getMin all O(1).
Q. Why MongoDB over SQL for your project? A. Flexible documents for evolving schema / nested data; acknowledge join and transaction trade-offs and when you would pick Postgres instead.
HR / managerial
Q. Two positives and two negatives a professor would say. A. Positives with evidence. Negatives that are real, owned, and improved (e.g. late starts → calendar blocking).
Q. Why Swiggy? A. Interest in high-scale marketplace systems (orders, assignment, SLAs); one concrete product observation; how your backend skills help reliability.
After technical rounds, Swiggy closes with hiring-manager and HR conversation. Prepare framed answers on the dedicated page.
Sources
These links are research inputs we used to map round structure and recurring questions - not copied page mirrors. We reorganized reports into process tables, outcome notes, and prep takeaways for Swiggy.
Candidate-reported SDE-2 backend threads (2025) on public engineering blogs and Q&A forums - treat compensation figures as unverified unless on offer letter.
