Amazon SDE VO (4/14): Timeline + Two-Round Recap (Locker & Meeting Rooms)

Round 1: Hiring manager

The interviewer was a Chinese hiring manager — serious but very professional. He joined 10 minutes late because of work and apologized sincerely right away, which actually helped me relax.

Behavioral

After about 10 minutes of introductions we moved to behavioral questions — three in total:

How did you handle a conflict with your team?
How did you deliver a high-pressure project under a tight deadline?
Do you have an example of using GenAI to improve your productivity?

Each question came with 2–3 follow-ups. I had prepared thoroughly with the STAR method, so my answers were well structured and concrete. The interviewer kept nodding, and this part went smoothly.

Coding: Locker & Package

The classic Locker & Package problem: design a locker system and decide whether a package fits.

I clarified the key points quickly, then explained my thinking while coding, keeping the structure clean and the communication flowing.
After the first version, the interviewer added a real-world follow-up; I adjusted the code and we discussed extensibility.
I finished by walking through a concrete example and analyzing time complexity.

Round 2

A friendly interviewer, with an Asian shadow interviewer observing.

Behavioral: cross-team collaboration deep dive

After 5 minutes of introductions, there was only one main question — but the follow-ups went very deep:

Tell me about a time you collaborated with a cross-functional team.

I used an example where I needed another team to provide a Map API to visualize tickets. The interviewer asked 6–7 follow-ups in a row; I broke it down step by step with STAR and he clearly approved. This took about 20 minutes.

Coding: Meeting Rooms series

Part 1: can all meetings be scheduled? — sort by start time and check whether adjacent meetings overlap.

Follow-up: the minimum number of rooms (Meeting Rooms II) — implemented with a min-heap, and I also described the sweep-line alternative. I proactively dry-ran an example and analyzed time and space complexity.

Takeaways

Amazon's behavioral follow-ups go deep — prepare your stories down to the details.
In coding, clarify proactively, talk while you code, and dry-run when you finish.