Interview Experience - 55 - Amazon | SDE II | L5
Summary:
📌 Job Role: Software Development Engineer II
🔢 Number of Rounds: 5
📜 Offer Status: Rejected
📍 Location: India
👤 Candidate Name: Not disclosing due to signed NDA
Interview Process
The interview process at Amazon consisted of five rounds: Telephonic, Managerial, System Design, Coding, and Low Level Design. Each round focused on different aspects of my skills and experience. The entire process was conducted virtually and spread over a few days. Each interview had a unique focus, ranging from coding problems and behavioral questions to detailed system design discussions.
Preparation Guide
I relied mostly on my previous experience and problem-solving practice for this interview. Although I didn’t refer to any one specific source intensively, consistent practice on platforms like LeetCode and prior exposure to real-world system design played a key role in my approach. I also made sure to prepare behavioral stories using the STAR (Situation, Task, Action, Result) format, which is quite useful in Amazon interviews that focus heavily on leadership principles.
Interview Rounds
Round 1: Telephonic
Duration: 60 minutes
Difficulty Level: Medium
Experience:
This round focused on algorithmic problem-solving. I was asked to solve two problems:
All nodes at distance K in a binary tree
LeetCode Article
This problem required understanding of tree traversal and BFS/DFS techniques.Maximum number of zeros in a row-wise sorted binary matrix
This tested optimization and understanding of matrix traversal to minimize unnecessary computation.
The interviewer was friendly and gave hints when I got stuck. I had to talk through my thought process while writing code and was asked to discuss time and space complexity after solving each problem.
Key Learnings:
Strong foundation in tree traversal (BFS, DFS) and matrix manipulation is critical.
Talking through your thought process helps the interviewer evaluate your problem-solving approach.
Make sure to test your code with edge cases within the interview itself.
Round 2: Managerial
Duration: 60 minutes
Difficulty Level: Medium
Experience:
This round had both behavioral and system design questions.
Behavioral:
Discussion around my current project and responsibilities.
Situational questions included:
A time when I proposed something and the manager did not agree.
A conflict with a colleague and how I handled it.
Gaining trust from a team member.
Design Question:
Design a ticketing system like BookMyShow
I had to talk about core features, seat selection logic, concurrency issues, and data modeling.
The interviewer was very focused on how I handled real-world situations and decision-making under pressure.
Key Learnings:
Prepare STAR-format stories for Amazon’s leadership principles.
Understand trade-offs and scalability considerations for even seemingly simple systems.
Show ownership and clarity in the reasoning behind technical and interpersonal decisions.
Round 3: System Design
Duration: 60 minutes
Difficulty Level: Hard
Experience:
The task was to design a blogging platform, and the discussion was in-depth and open-ended.
I was asked to think through and explain:
User-specific feature sets.
Home page layout for signed-in vs anonymous users.
Blog recommendation logic.
Trending blog identification.
Search feature implementation across and within blogs.
Service decomposition strategy.
Choice of database (SQL vs NoSQL).
Handling abuse from anonymous users.
Monitoring setup, alert thresholds, and severity classification.
Final question: Why do you want to join Amazon?
This was a very detailed round that required thinking from a product, architectural, and operational standpoint.
Key Learnings:
Maintain a structured approach: Functional requirements → Components → DB design → Scaling → Monitoring.
Justify every design decision with trade-offs.
Expect deep dives into both architectural and non-functional requirements.
Round 4: Coding
Duration: 60 minutes
Difficulty Level: Medium
Experience:
The round included algorithmic problems along with behavioral questions.
Problems:
Three Sum problem – Find 3 numbers in an array which sum up to a specific value.
LeetCode ProblemCleaning robot simulation – Simulate a robot that can rotate and detect blockages or dirt. It had to clean all reachable dirty blocks from any starting position.
Behavioral:
A time when I went above and beyond my role.
A situation where I exceeded customer expectations.
The robot problem required careful simulation logic and edge case handling.
Key Learnings:
Array problems like 2-sum, 3-sum are common and important.
Simulation problems test logical thinking and attention to constraints.
Behavioral questions are often tied to Amazon’s leadership principles — prepare accordingly.
Round 5: Low Level Design
Duration: 75 minutes
Difficulty Level: Hard
Experience:
This was a mixed round with both low-level design and coding problems.
Low Level Design:
Design a food delivery platform like Zomato
I was asked to talk through entity design, data flow, interactions between delivery partner and customer, and scalability.
Coding Problems:
Two knights on a chessboard – Determine which one reaches the destination first.
Analyze viewership data – Given segments of a video watched by users, return segments watched more than k times.
Behavioral: A time when I had to work with incomplete data or information.
This round tested breadth and depth across multiple skillsets in a single session.
Key Learnings:
Be thorough with LLD concepts like class diagrams, responsibility segregation, and extensibility.
Be prepared to switch contexts quickly between design, coding, and behavioral discussion.
Have experience-based stories ready for ambiguous or data-sparse situations.
Final Thoughts
This interview process was one of the more rigorous ones I’ve faced. Amazon places strong emphasis not only on technical abilities but also on how candidates align with their leadership principles. Preparation for Amazon should be balanced across:
Problem solving: Practice trees, arrays, dynamic programming, and simulation problems.
System design: Learn to break down large-scale systems, identify components, and discuss trade-offs.
Behavioral: Have well-thought-out experiences for all major leadership principles.
Even though I did not receive an offer, the experience helped me identify areas for improvement, especially in articulating my decisions during system design and behavioral rounds.