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Swiggy Interview Questions (36)

Practice real interview problems from Swiggy

#12

Integer to Roman

Medium✓ Solution📹 Video
Accenture+30
#72

Edit Distance

Medium✓ Solution📹 Video
Amazon+23
#75

Sort Colors

Medium✓ Solution📹 Video
Agoda+36
#88

Merge Sorted Array

Easy✓ Solution📹 Video
Accenture+36
#146

LRU Cache

Medium✓ Solution📹 Video
Accenture+119
#152

Maximum Product Subarray

Medium✓ Solution📹 Video
Adobe+22
#202

Happy Number

Easy✓ Solution📹 Video
Accenture+26
#371

Sum of Two Integers

Medium✓ Solution📹 Video
Amazon+7
#496

Next Greater Element I

Easy✓ Solution📹 Video
Accenture+16
#560

Subarray Sum Equals K

Medium✓ Solution📹 Video
Accenture+46
#888

Fair Candy Swap

Easy✓ Solution📹 Video
Amazon+5
#1174

Immediate Food Delivery II

Medium✓ Solution📹 Video
Amazon+7
#1711

Count Good Meals

Medium✓ Solution📹 Video
Amazon+3
#2943

Maximize Area of Square Hole in Grid

Medium✓ Solution📹 Video
Amazon+3
#3169

Count Days Without Meetings

Medium✓ Solution📹 Video
Amazon+3

About Swiggy Coding Interviews

Swiggy is one of India’s leading food delivery platforms, operating at massive scale with millions of daily orders and complex logistics. Because of this scale, Swiggy’s engineering teams place strong emphasis on data structures, algorithms, and real-world problem solving. Engineers are expected to build highly reliable systems that optimize delivery routes, handle real-time order matching, and process large volumes of data efficiently.

The Swiggy coding interview process typically evaluates a candidate’s ability to write clean, efficient code while reasoning through edge cases and performance constraints. Interviewers often focus on practical algorithmic patterns that mirror real operational challenges—such as optimizing delivery paths, managing queues of orders, or processing streaming data.

Across real candidate reports, Swiggy interview questions frequently involve:

  • Arrays and Hashing for fast lookups and frequency-based problems
  • Graphs and BFS/DFS for route optimization and connectivity problems
  • Greedy algorithms for scheduling and resource allocation
  • Heap/Priority Queue problems for task ordering and real-time processing
  • Dynamic Programming for optimization-heavy scenarios

Difficulty usually spans a mix of medium and hard problems, with an emphasis on writing production-quality code rather than only explaining theory. Candidates are often expected to discuss time and space complexity and improve an initial brute-force approach.

FleetCode helps you prepare efficiently by compiling 41 real Swiggy interview questions asked in past interviews. Each problem is categorized by difficulty and includes optimized solutions in Python, Java, and C++. Practicing these patterns helps you recognize the exact types of algorithmic challenges Swiggy engineers use during coding interviews.

Interview Tips for Swiggy

Preparing for a Swiggy coding interview requires a mix of strong DSA fundamentals and the ability to reason about real-world scale problems. The company tends to favor candidates who can move from a simple approach to an optimized solution while explaining their thinking clearly.

Typical Swiggy interview process:

  • Online assessment or coding round (60–90 minutes): Usually 2–3 algorithmic problems focused on arrays, graphs, or greedy techniques.
  • Technical interview rounds (2–3 rounds): Live coding with deeper discussion of complexity, edge cases, and code quality.
  • System design or architecture round: For experienced roles, expect discussions around scalable systems like food delivery matching or real-time order tracking.
  • Hiring manager or culture round: Focuses on ownership, collaboration, and problem-solving mindset.

Most common DSA topics asked at Swiggy:

  • Graphs (shortest path, traversal, connectivity)
  • Priority queues and heaps
  • Sliding window and two-pointer techniques
  • Hash maps and frequency counting
  • Greedy algorithms and scheduling
  • Dynamic programming for optimization problems

A strong preparation strategy is to first master medium-level problems in arrays, graphs, and hashing, then move into harder optimization and graph-based questions. Swiggy interviewers often ask follow-up questions like improving time complexity or handling large datasets.

Common mistakes candidates make:

  • Jumping into coding without clarifying constraints or edge cases
  • Ignoring time and space complexity discussion
  • Not explaining trade-offs between different approaches
  • Writing code that works but lacks readability or modular structure

For most candidates, a 6–8 week preparation timeline is effective. Start by revising core data structures, then practice company-tagged questions and timed coding sessions. Working through real Swiggy interview problems—like the 41 curated questions on FleetCode—helps you recognize recurring patterns and build the confidence needed for the actual interview.