Interview Experience at Amazon - SDE 1

This is a detailed account of my Amazon interview journey from the online assessment (OA) to the final bar raiser round. I wrote this as a technical, practical guide for other engineers preparing for Amazon interviews.

Online Assessment (OA) — July 12

The OA was on July 12. I received two problems; both were graph themed and roughly LeetCode medium difficulty.

Problem 1: I solved it and my submission passed all test cases.
Problem 2: I implemented a correct approach but only passed 3/15 test cases during the OA (edge cases/time/optimization issues).

Important note: plagiarism is strictly prohibited. Amazon uses tooling to detect copied solutions. Always write your own code, document your thought process, and avoid submitting copied snippets or public repository solutions. If they detect plagiarism, you risk immediate disqualification.

After the OA, on July 15 I received a message that I had cleared it and was requested to fill the interview scheduling form.

First technical interview — early August

Scheduling: The first interview happened in the first week of August.

Interviewer tone: The interviewer was polite, calm, and helpful — which made it easier to talk through the problem.

The technical question: another graph style problem. I solved it using BFS in about 25–30 minutes. It wasn’t the optimal solution, but I provided a clear pseudo code outline for the optimal approach and explained complexity trade offs.

What helped in this round:

• Talk through your approach before coding. Communicate the data structures and expected complexity.
• If your solution is sub optimal, present the optimal idea as a follow up interviewers appreciate awareness of trade offs.
• Keep talking: narrate what you test and why.

After solving the problem we spent some time discussing generative AI and system level ideas the interview was conversational and not rushed.

Second technical interview — mid August

I was informed in the second week of August that I cleared the first round and moved to the second round.

Format: Same warm introductions, then two parts a technical question and a behavioural segment heavily focused on Amazon Leadership Principles.

Technical question: This round had a queue based question which I found easier than the first round. I explained my approach and coded the solution with tests.

Behavioural focus: The interviewers asked several behavioural questions tied to leadership principles. Each behavioural prompt often led to five or more follow up questions they dig into detail.

Key guidance for behavioural questions:

• Use the STAR format: Situation, Task, Action, Result. Be concise but specific.
  Prepare for deep follow ups: they will probe decisions, trade offs, and the impact of your actions.
• Focus on metrics and outcomes where possible (customer impact, performance improvements, time saved, user satisfaction).

Note: If you waffle or give unfocused answers, you risk being rated poorly. Candidates have been eliminated for vague behavioural answers prepare concrete examples ahead of time.

After about 1.5 weeks, I was told I cleared the second round and was scheduled for the third round (the Bar Raiser) one week later.

Bar Raiser — the unique Amazon round

What is a Bar Raiser? This is an Amazon specific interview whose goal is to determine whether a candidate raises the performance bar for the organisation. Essentially, the bar raiser evaluates whether you’d be in the top ~50% (or better) of engineers at your level.

Format: This round is primarily behavioural and situational. There may be follow ups and challenging hypothetical questions.

Example bar raiser questions I got:

• “Tell me about a time when a teammate was not contributing — how did you convince them to work effectively with you?”
• “Tell me about a time when an end user gave feedback and you changed the project accordingly.”

Why these questions matter:

• They’re not trying to trip you up with technical puzzles; they’re looking for consistent behaviours that match Amazon Leadership Principles — especially Customer Obsession, Ownership, Earn Trust, and Deliver Results.
• The bar raiser is evaluating depth: how you influence others, how you make trade offs, and whether you demonstrate long term thinking.

How to prepare for the bar raiser:

1. Catalogue examples across leadership principles: have 8–12 stories ready (2–3 per major principle).
2. Practice STAR answers with metrics: what was the measurable outcome?
3. Expect detailed follow ups: know the timeline, stakeholders, constraints, and alternatives you considered.
4. Be explicit about the customer: explain why the change benefited users.

Behavioural tips & common pitfalls

• Always answer behavioural prompts with STAR; avoid rambling.
• Be specific about your actions — avoid ‘we’ without clarifying your role.
• When asked about trade offs, give alternatives you considered and why you chose one.
• If you made a mistake, own it and explain the lesson learned plus corrective actions.

Common reasons candidates are rejected:

• Vague behavioural answers with no measurable results
• Over reliance on copied code during OA (plagiarism)
• Poor debugging or inability to reason about edge cases in technical rounds
• Not considering customer impact or long term operational costs

Quick technical advice for OA & interviews

• Practice graph and queue problems (BFS, DFS, topological sort, shortest paths) — many OA questions use these.
• Write clean, well commented code during the OA; include edge case handling where possible.
• When your first submission fails some tests, don’t panic, instead explain what you would improve if you had more time.
• Mock interviews help: practice explaining your choices aloud.

Final thoughts

My interview process was a mix of technical rigor and deep behavioral evaluation. The OA tested problem solving under time pressure; the interviews tested both technical depth and cultural fit. The bar raiser was the most unique step — it ensured the organisation only hires people who can raise the bar.

If you’re preparing for Amazon, focus equally on coding fundamentals and behavioural stories. Practice STAR, own your code, and be honest — the interviewers value clear thinking and ownership.

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