Amazon | SDE 2 | Recent Interview Experiences | 2026 | Set-60

Question

Status: SDE2 at Mid-sized Tech

 

Experience: 4 Years

 

Position: SDE2 (L5) at Amazon

 

Date: February 2026

 

Online Assessment (OA)

Two coding questions + Work Style Simulation.

 

Question 1: Variation of Merge Intervals.

 

Question 2: Graph/Dijkstra based.

 

Simulation: Focus heavily on Ownership and Bias for Action. Don't just pick the "safe" answer; pick the one that delivers for the customer.

 

Virtual Onsite (4 Rounds)

Round 1: Data Structures & Algorithms

Leadership Principles (LP): Customer Obsession and Ownership.

 

Technical: LeetCode 1094 (Car Pooling) variant.

 

Details: After solving with a TreeMap (O(NlogN)), the interviewer pushed for an O(N) solution using a difference array. We then discussed how to scale this if the "trips" data was stored in a distributed DB.

 

Round 2: System Design (HLD)

LP: Learn and Be Curious and Dive Deep.

 

Technical: Design a Real-Time Error Log Monitoring System.

 

Details:

 

Requirements: Handle 1M+ writes/sec, sub-second alerting.

 

Architecture: Used Kafka for the ingestion layer, Flink for windowed aggregation, and Elasticsearch for querying.

 

Deep Dive: We spent 15 minutes on backpressure—what happens when the consumer can't keep up with the log spikes?

 

Round 3: Object-Oriented Design (LLD)

LP: Bias for Action and Disagree and Commit.

 

Technical: Design a Vending Machine Leasing System.

 

Details: Focused on the relationship between VendingMachine, LeaseAgreement, and PaymentStrategy.

 

Key: Used the State Pattern to handle machine conditions (Idle, Out of Stock, Maintenance). The interviewer looked for clean interfaces and extensibility.

 

Round 4: The Bar Raiser

LP: Insist on the Highest Standards and Deliver Results.

 

Technical: Distributed Rate Limiter.

 

Details: * Started with the Token Bucket algorithm logic.

 

Moved into the implementation: How to handle concurrency using Redis Lua scripts to ensure atomicity.

 

The LP part was intense; they kept asking for specific data points and metrics for every "Result" I mentioned.

 

Result: Offer.