Flipkart Interview Experience for BA intern

Overview of Interview Process:Initial ScreeningThe process began with an online assessment consisting of 45 MCQs covering aptitude, verbal ability, and core computer scien...

Overview of Interview Process: Initial Screening The process began with an online assessment consisting of 45 MCQs covering aptitude, verbal ability, and core computer science topics. There were also 5 SQL coding questions, mostly based on JOINs, which were of easy to medium difficulty. All rounds in the process were eliminatory.

Technical Round 1 The interview started with brief introductions and then moved into a discussion about one of my projects from a Myntra hackathon. The interviewer turned this into a case study-style question, asking about the project’s objective and how I would measure its success using metrics like traffic, engagement, and conversion rates. We also spoke about a few of my other hackathon projects. Next, I was given a medium-level SQL question that required using joins and window functions such as RANK. I was asked to explain the difference between RANK, DENSE_RANK, and ROW_NUMBER. The interviewer pointed out a few mistakes and gave hints along the way, and I was able to solve it by the end. Practicing questions on platforms like DataLemur was definitely useful for this part. Towards the end, there was a product case question based on Instagram, and the scenario involved proposing a new feature that lets users react in multiple ways, similar to LinkedIn, instead of only liking a post. I had to explain how I would convince the Meta team to add this feature using data-backed reasoning, focusing on engagement, retention, and conversion metrics. I received the link for the

next round about an hour and a half after the this one ended.

Technical Round 2 This round also involved discussions around my projects and two case study questions. The first was about Flipkart setting up a dark store (like Blinkit) in my city with limited storage space. I had to explain how I would decide 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. From what I heard from other candidates, the questions varied a lot depending on the interviewer since some were asked probability or guesstimate questions as well.