Synthesized from 27 candidate submissions for ALL.
Flipkart’s SDE-2 hiring process is rigorous and multi-layered, with a strong emphasis on system design, low-level design (LLD), machine coding, and DSA. The process typically includes 4-5 rounds: machine coding, DSA, system design (LLD + HLD), and a hiring manager round. Candidates are expected to demonstrate scalability, modularity, and clean code practices in machine coding rounds, while system design rounds test their ability to design large-scale systems.
InterviewDigestprocessOrder, fetchTransaction, getOrders.The intern hiring process at Flipkart typically includes an Online Assessment (OA) followed by 2-3 technical rounds and a managerial round. The OA tests aptitude, verbal ability, and core CS topics, including SQL. Technical rounds focus on projects, SQL queries, product case studies, and business case studies, with an emphasis on metrics-driven reasoning. The process is eliminatory, with quick feedback loops between rounds.
Flipkart’s SDE-3 hiring process is designed for senior engineers with a strong emphasis on system design, scalability, and leadership. The process typically includes a machine coding round, DSA round, system design round, and a hiring manager round. Candidates are expected to demonstrate deep technical expertise, clean code practices, and the ability to design large-scale systems.
Flipkart’s SDE-1 hiring process is highly competitive, with a strong focus on Data Structures and Algorithms (DSA), problem-solving, and system design. The process typically includes an Online Assessment (OA), 2-3 technical rounds, and a managerial round. The OA is often platform-based (e.g., SHL) and includes medium-to-hard DSA problems. Technical rounds test DSA, machine coding, and problem-solving skills, while the managerial round evaluates behavioral fit and project discussions.
Flipkart’s UI2 (Product/Frontend) hiring process is highly specialized, evaluating frontend technologies, system design, and product sense. The process includes 5 elimination rounds: machine coding, UI tech (DSA and UI/React), product sense, and a hiring manager round. Candidates are expected to demonstrate strong frontend skills, UI/UX knowledge, and the ability to design scalable frontend systems.
The candidate went through two technical rounds after clearing an online assessment. The rounds involved discussions on projects, SQL queries, product case studies, and business case studies. The interviewer provided hints and feedback during the rounds. The process was eliminatory, and the candidate received the next round link within an hour and a half after the first technical round.
Online assessment consisting of 45 MCQs covering aptitude, verbal ability, and core computer science topics. Also included 5 SQL coding questions, mostly based on JOINs, with easy to medium difficulty.
The interview began with introductions and a discussion about a project from a Myntra hackathon, which was turned into a case study. The interviewer asked about the project’s objective and success metrics like traffic, engagement, and conversion rates. A medium-level SQL question involving JOINs and window functions (RANK, DENSE_RANK, ROW_NUMBER) was discussed. A product case question about proposing a new feature for Instagram (multi-reaction system) was also addressed, focusing on data-backed reasoning for engagement, retention, and conversion metrics.
Discuss a project from a Myntra hackathon and how you would measure its success using metrics like traffic, engagement, and conversion rates.
Medium-level SQL question involving JOINs and window functions (RANK, DENSE_RANK, ROW_NUMBER).
Product case question: Propose a new feature for Instagram that allows users to react in multiple ways (similar to LinkedIn) instead of just liking a post. Explain how you would convince the Meta team using data-backed reasoning focused on engagement, retention, and conversion metrics.
This round involved discussions around projects and two case study questions. The first case study was about Flipkart setting up a dark store (like Blinkit) in the candidate's city with limited storage space, requiring a decision on what items to stock using Flipkart’s purchase data. The second case study was about evaluating the success of a new loyalty program and distinguishing whether improvements were due to the program itself or seasonal factors.
Case study: Flipkart setting up a dark store in your city with limited storage space. How would you decide what items to stock using Flipkart’s purchase data?
Case study: Evaluating the success of a new loyalty program and distinguishing whether improvements were due to the program itself or seasonal factors.
Probability or guesstimate questions (varied by interviewer).
The candidate progressed through three rounds: a coding round, a technical round, and a technical + managerial round. They were selected for the final offer along with 2 other candidates.
A 90-minute coding round featuring 2 medium-level problems from LeetCode and 1 hard-level problem similar to 'Rat in a Maze'. 13 candidates were selected for Round 2, and 10 candidates were placed on the waitlist for Round 2.
2 medium-level problems from LeetCode
1 hard-level problem similar to 'Rat in a Maze'
The interview began with brief introductions followed by technical questions. Problem 1: Find the largest length of a subsequence in an array where (max element - min element = 1). The candidate used hash maps to solve the problem and discussed time and space complexities for both ordered and unordered maps. Problem 2: Find the element at the nth row and kth index in Pascal's Triangle. The candidate explained the intuition and derived the formula.
Find the largest length of a subsequence in an array where (max element - min element = 1).
Find the element at the nth row and kth index in Pascal's Triangle.
The round began with an elaborate self-introduction, detailing internship projects, experiences, and additional projects. The candidate discussed specific tasks from their internship, demonstrated dataset attributes using an Excel sheet, and shared key learnings and mentor’s role. Additional questions included alignment of internship choice with machine learning interests, strengths, weaknesses, academic performance, and preparation strategies for Flipkart. Technical questions included differences between SQL and NoSQL databases with examples, and the format of data storage in MongoDB.
Reason for interning in a steel industry despite pursuing Electronics and Communication.
Shared strengths, weaknesses, and proudest achievements.
Discussed SGPA trends and overall academic performance over semesters.
Reasons for choosing Flipkart.
Preparation strategies for the SDE role at Flipkart.
Differences between SQL and NoSQL databases with examples.
Format of data storage in MongoDB.