Amazon | SDE-1 (L4 - New Grad) | Bangalore | Jan 2025 Offer | Select ✅

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sde-1bangalore0.9 yearsOffer
December 12, 202450 reads

Summary

I successfully received an Amazon SDE-1 offer for a New Grad position in Bangalore, India, after an off-campus hiring process. The selection involved an Online Assessment, two Technical Rounds focusing on Data Structures & Algorithms, and a final Bar Raiser round primarily assessing Amazon Leadership Principles.

Full Experience

Background

I completed my BTech in CSE-AI in the 2024 batch. My professional experience includes a 6-month internship at SAP Labs India (Jan 2024 - July 2024), followed by 5 months of full-time employment at SAP Labs India (July 2024 - Jan 2025), totaling about 11 months of experience. This Amazon Offcampus offer came from a Tier 3 college, which was a significant achievement for me.

Process Overview

The journey began when I filled out the Hiring Interest Form on November 1, 2024, and was subsequently invited to apply for the role on their Jobs portal the next day. After resume shortlisting, I proceeded to the Online Assessment.

Online Assessment (Nov 4, 2024)

I had 5 days to complete this assessment on HackerRank, which consisted of two coding questions and over 50 behavioral questions.

Virtual Interview Rounds

I was notified on November 11, 2024, that my interviews were being scheduled. The interview process comprised a maximum of three eliminatory rounds, each lasting one hour: two technical rounds and one Bar Raiser round. The assessment areas included Data Structures & Algorithms, Problem Solving, Coding, Amazon Leadership Principles, and Behavioral questions.

Technical Round 1 (Nov 12, 2024)

After a brief introduction from both the interviewer (an SDE with 4+ years at Amazon) and myself, we moved directly to coding questions on their Livecode platform. I could choose any language.

I successfully cleared this round and was notified via mail on Nov 15, 2024.

Technical Round 2 (Nov 18, 2024)

This round had two interviewers and began immediately with two behavioral questions, followed by two coding questions. For one coding question, I was expected to provide optimized code, and for the other, just the approach. The behavioral questions, with unexpected follow-ups, took up the first 30 minutes, making me a bit nervous.

I cleared this round as well, receiving an email on Nov 19, 2024, stating I was moving to the Final Round of the Loop Interview.

Bar Raiser Round (Nov 21, 2024)

The interviewer was an SDM with over 19 years of total experience, including 7+ years at Amazon. He skipped my self-introduction and directly explained the interview structure: three behavioral situations will be assessed. These were somewhat unconventional questions.

The round concluded in about 40 minutes, and I was confused about whether my quick conversation would lead to rejection, especially after Googling the 20% success rate for Bar Raiser rounds.

Offer and Outcome

A few days later, on November 26, 2024, I received a call from the recruiter confirming I had cleared all rounds and providing an offer! We discussed locations (Bangalore or Chennai) and my notice period. I chose Bangalore, with a starting date of January 6, 2025. I received the final offer letter on December 2, 2024, and happily accepted it within a few days.

Interview Questions (11)

Q1
Password Similarity Check
Data Structures & Algorithms

Given two arrays of passwords, determine if each old password can be transformed into a subsequence of the corresponding new password by incrementing specific characters cyclically. Return "YES" or "NO" for each pair.

Q2
Maximize Warehouse Efficiency
Data Structures & Algorithms

Given an array of parcel weights, calculate the maximum efficiency by repeatedly selecting and removing either the first or last parcel in the array. The sum of selected parcels determines the efficiency. Return the maximum possible efficiency.

Q3
Complete Binary Tree Node Count
Data Structures & Algorithms

Find the total number of nodes in a given complete binary tree efficiently.

Q4
Sort String by Frequency
Data Structures & Algorithms

Sort characters in a string in decreasing order of their frequency. If multiple answers exist, return any valid one.

Q5
Aggressive Cows (Max Distance)
Data Structures & Algorithms

Place k cows in stalls to maximize the minimum distance between any two cows.

Q6
Directed Graph Edge Score
Data Structures & Algorithms

Find the node with the highest edge score in a directed graph. If there's a tie, return the smallest indexed node.

Q7
Most Played Songs
Data Structures & Algorithms

Create a playlist of the most frequently played songs for a user based on the play count.

Q8
Longest Repeating Substring
Data Structures & Algorithms

Find the longest duplicated substring in a given string. If no such substring exists, return an empty string.

Q9
Leading Initiatives Outside Daily Scope
Behavioral

How did you take up or lead something which is out of your daily scope of work?

Q10
Overcoming Negative Feedback
Behavioral

How did you overcome a situation where you have been given a negative feedback?

Q11
Approach to Learning New Things
Behavioral

Where/How do you start learning which is something new to you?

Preparation Tips

Preparation Strategy

My preparation focused on a few key areas to tackle the Amazon interview process effectively.

Data Structures & Algorithms (DSA)

I dedicated significant time to practicing various DSA problems. My recent problem-solving sessions proved beneficial, as I had just solved a question like "Aggressive Cows," which appeared in my first technical round. I aimed to understand the brute-force approaches first, then optimized solutions, and also practiced discussing time and space complexities.

Problem Solving & Coding

I honed my problem-solving skills by practicing on platforms like LeetCode. The goal was not just to find a solution but to articulate it clearly, consider edge cases, and be able to code it efficiently under time pressure. The interview experience required me to provide not just code but also discuss alternative approaches and perform dry runs.

Amazon Leadership Principles & Behavioral Questions

For the behavioral aspect, which was crucial in the OA, technical rounds, and especially the Bar Raiser round, I focused on understanding Amazon's 16 Leadership Principles. My strategy for answering these questions was to strictly follow the STAR (Situation, Task, Action, Result) technique. This helped me structure my responses effectively with real-life examples, although I did find some questions challenging, requiring quick thinking and adaptability.

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