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My Amazon Software Development Engineer New Grad Interview Experience

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  • Name
    Tharun Kumar

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Interviewing with Amazon for the SDE 1 New Grad position was one of the most anticipated and growth-oriented experiences of my career journey so far. Known for its bar-raising culture and customer obsession, Amazon challenged me not only on my technical foundation but also on my problem-solving mindset and ability to dive deep into a problem before making a decision.

In this post, I’ll walk you through my interview process step-by-step, from the initial application to the final decision, along with the lessons I learned and insights that may help you on your own journey.

📝 Interview Process Overview

I applied for multiple SDE New Grad (2025 Start) roles on the Amazon Jobs portal as soon as they opened, with a referral from a friend of mine who works as a manager on the Marketing Analytics team of Amazon (Yes, remember this, referrals need not be from same role type you’re desiring e.g., I applied for a software engineering role with a referral from marketing analytics manager). Amazon has a rolling application system, so early applications definitely help increase visibility for your profile.

Whole process in a nutshell:

  • Online Application Submitted
  • Online Assessment (OA)
  • Amazon University Talent Acquisition(AUTA) email
  • SDE FTE Interview Invitation Survey
  • Virtual Onsite Interviews (3 Rounds)
  • Final Outcome

The Online Assessment (OA)

1. Coding Assessment (2 Questions)

Format:

  • 2 coding questions with any language of choice(I chose Python)
  • Difficulty: Medium
  • Duration: 70 minutes
  • Platform: Hackerrank

Question 1: Strategic Damage Mitigation

This problem involved simulating a multi-round scenario where a player loses health in each round and is allowed a one-time usage of a damage-mitigating tool. The goal was to determine the minimum starting value that ensures survival through all rounds, using the tool optimally.

What this question tested:

  • Greedy decision-making for optimization
  • Array traversal and conditional logic
  • Edge case thinking (e.g. when the most damaging round is early or late)

What Helped Me Succeed:

  • Summing up the total cost and then thinking about where to apply the mitigation for maximum impact
  • Debugging small test cases manually to validate logic before submitting

Question 2: Linked List Reordering with Conditions

This challenge involved reordering nodes in a singly linked list based on certain constraints. The twist was that the reordering wasn’t just positional — it also involved conditional grouping and maintaining relative order.

What this question tested:

  • Linked list traversal and pointer manipulation
  • Understanding of dummy nodes and in-place updates
  • Attention to detail is required to maintain node connections correctly

What Helped Me Succeed:

  • Drawing the list structure out on paper before coding
  • Using separate dummy head nodes to simplify edge cases
  • Testing with single-node, two-node, and alternating condition cases to validate correctness

2. Work Style Assessment

Format: Multiple-choice personality-style questions (~20 minutes)  This section focuses on evaluating alignment with Amazon’s Leadership Principles. There were no right or wrong answers, but consistency and honesty mattered.

What Helped Me Succeed:

  • Thinking from a customer-obsession and team-first mindset
  • Read about Amazon’s 16 leadership principles beforehand
  • Being consistent across similarly framed questions

3. Work Simulation

Format: Role-based scenarios (~35–40 minutes)  In this section, I was presented with a series of realistic workplace situations, emails, messages, and stakeholder updates. I had to pick the most effective and possible response from multiple choices.

What It Tested:

  • Decision-making in ambiguous scenarios
  • Prioritization skills under pressure
  • Ownership, communication, and leadership judgment

What Helped Me Succeed:

  • Read each question twice before answering it
  • Think in terms of long-term impact, team collaboration, and doing the right thing. The most important: try to think how Amazonians think

💻 👥 Virtual On-site Interviews

After successfully passing the OA, I received an email from the Amazon University Talent Acquisition(AUTA) team asking to schedule a time to conduct the virtual on-site interviews. The next step was a virtual onsite, consisting of three 1-hour rounds.

On the day of the interview, I got an email mentioning that: due to a last-minute conflict, your 1st interview for today will be rescheduled to another date. Please proceed with the other 2 rounds as scheduled, and please share your availability for the next 2 weeks so that we can reschedule your missed interview.

Later, I got my first coding round conducted 3 days after receiving the above email. Here’s the detailed breakdown of the interview rounds:

💻 Round 1: Coding + Object-Oriented Design

Format:

  • Duration: 1 hour long
  • Environment: Amazon Chime + Amazon LiveCode
  • Structure: 1 OOD design question + a short discussion toward the end

This round focused on designing a system using object-oriented design(OOD)/Low-Level Design(LLD) principles. I had to identify the right classes, define appropriate attributes and methods, and ensure that all constraints were enforced (like limits on resource usage, access control, and tracking).

What this round tested:

  • Object-oriented design(Low Level design) thinking
  • Modelling real-world constraints into clean class structures
  • Effective use of inheritance, encapsulation, and data validation

What Helped Me Succeed:

  • Practicing system design questions involving entities, rules, and relationships
  • Keeping my code readable and modular
  • Thinking out loud and confirming my design assumptions with the interviewer

💻 Round 2: Data Structures & Logic Implementation

Format:

  • Duration: 1 hour long
  • Environment: Amazon Chime + Amazon LiveCode
  • Structure: 2 coding questions + a short discussion toward the end

This round had two back-to-back questions:

Question 1:

A real-time simulation of data ingestion and querying was involved. I was expected to build the system logic to efficiently store(selecting an efficient data structure, e.g., I used OrderedDict in Python in this round) and query incoming stream updates. There were additional requirements like aggregation, top-K selection, and maintaining historical snapshots.

What it tested:

  • Use of hash maps and lists for structured time-series data
  • Writing efficient queries with filtering and sorting
  • Edge case handling for missing or invalid input

Question 2:

This question focused on efficient item packing, where items with certain weights needed to be grouped optimally without exceeding capacity. The problem demanded both greedy and analytical thinking.

What Helped Me:

  • Explaining my thought process before coding
  • Walking through custom test cases to validate edge behaviour
  • Clearly stating time and space complexity at the end

👥 Round 3: Managerial / Bar Raiser Interview

Format:

  • Duration: 1 hour long
  • Environment: Amazon Chime
  • Structure: Amazon Leadership Principles, Behavioral questions, values alignment

This was a behavioral deep dive where I was expected to discuss my past work, decision-making process, and how I handle complexity. The interviewer followed up on each answer with layered questions to gauge depth and self-awareness.

Areas accessed during this round:

  • How I take ownership in ambiguous situations
  • How I respond to failure or constructive feedback
  • How do I make trade-offs between fast execution and long-term quality
  • What drives me to build scalable and meaningful systems

What Made It Challenging:

Midway through the interview, I found myself mentally saturated. The expectations for depth and reflection were high, and I had already spoken about my most impactful projects. It taught me how important mental energy management is during interviews.

What Helped Me:

  • Structuring every response using the STAR method
  • Being honest and not trying to “make up” the answers
  • Sharing both wins and learnings from failures I faced
  • Tying experiences back to impact, ownership, and long-term growth
  • Talking about the feedback I received from mentors and how I improved

💡Lessons learned & Tips for You

What Worked:

  • Practicing real interview-style problems under timed conditions
  • Able to come up with a good object-oriented design under pressure
  • LeetCode (Amazon Tagged + Blind 75 + Grokking Coding Patterns)
  • Clear explanation of Time and space complexity tradeoffs, not just brute force solutions

Key Takeaways from my viewpoint:

  • Object-oriented design interviews are more about thinking clearly than writing perfect code
  • In coding rounds, testing your code thoroughly(without being prompted by the interviewer) can leave a strong impression
  • Managerial interviews demand deep introspection — prepare 3–4 strong stories and know them well
  • Even if you perform well, some rounds may weigh more than others. Don’t take rejection personally
  • Most importantly, never stop reflecting and refining your approach

Amazon 2 & 5 Promise

While preparing for my interviews, I came across Amazon’s Interview Guide, which emphasized something called the 2 & 5 Promise — Amazon’s internal commitment to candidates that you’ll hear back within 2 business days of the phone screen interview being completed, or within 5 business days for on-site interviews

I realized how important it is to read these guides before interviewing with any company, as they often reveal not just the process, but also the culture and expectations.

I kept this in mind throughout my experience. After my interviews wrapped up, I proactively followed up with the recruiting team, politely referencing the 2&5 promise. This showed that I had done my homework and valued Amazon’s candidate experience just as much as they value customer experience. While the response eventually confirmed a rejection, the clarity and timeliness in the communication reflected Amazon’s bar-raising approach to transparency and process.

Lesson to learn: Always read the company’s official interview prep material because it helps you understand not just how to prepare, but also how to engage professionally during the hiring process.

📚 Resources That Helped

✨ Final Thoughts & Learnings

The overall experience taught me how high the bar really is at Amazon and made me even more determined to push my skills, refine my storytelling abilities, and build deeper system design fluency.

Although I put in my best effort, performed well in technical rounds, and had thoughtful reflections in the behavioral round, I didn’t land the Amazon offer. I walked away with immense clarity, stronger coding fluency, a renewed mindset and importantly, behavioral storytelling. It taught me that every interview is a learning opportunity, and I understood that success isn’t just the offer letter; it’s what you gain and build along the way.

To everyone preparing to get that one job offer: keep going, reflect deeply on the feedback received, and never underestimate your progress and potential. Every rejection brings you closer to the role that truly fits.

Just Remember: You don’t fail when you fall, you fail only when you stop rising and Each “no” brings you one step closer to the right “yes”. Trust me!

Keep an eye out for my upcoming blog posts, where I’ll share more of my experiences and resources to help you grow both technically and professionally. You won’t want to miss these valuable tips to help you on your journey and land the opportunities you deserve!

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