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Amazon·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate
Jul 2026

Summary

Amazon SWE interview with a product design question that felt more PM-ish than I expected for an engineering role.

Questions Asked (1)

Q1

How would you design the front page of a newspaper app?

System DesignProduct Sense & IdeationTechnical Trade-offs
Author's notes

Spent the first few minutes just listing features which was probably the wrong move.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scope, then outline a high-level architecture that covers data flow, storage, and serving. Focus on scalability, low latency, and personalization, and discuss trade-offs between different design choices.

Pro tip: Emphasize how you would leverage AWS services like CloudFront, S3, and DynamoDB to achieve low latency and high availability, and discuss how to handle peak traffic with auto-scaling and caching.

1. Clarify Requirements

Ask questions to understand the scope: expected user base, content types (articles, images, videos), personalization needs, offline support, and update frequency.

2. High-Level Architecture

Propose a client-server architecture with a CDN for static assets, API gateway for dynamic content, and microservices for different functionalities (e.g., content delivery, user profile).

3. Data Storage and Retrieval

Discuss database choices (e.g., DynamoDB for user data, S3 for media, Elasticsearch for search) and caching strategies (Redis, CDN) to ensure low latency.

4. Personalization and Recommendations

Explain how to implement personalized content using user behavior data, machine learning models, and real-time processing (e.g., Kinesis, Lambda).

5. Scalability and Reliability

Address auto-scaling, load balancing, fault tolerance, and monitoring to handle traffic spikes and ensure high availability.

Key Points to Mention

  • Use of CDN (e.g., CloudFront) for static content and caching
  • Database choices: NoSQL for scalability (DynamoDB), search (Elasticsearch)
  • Microservices architecture for modularity and independent scaling
  • Personalization via user data and ML models
  • Caching strategies (Redis, in-memory) to reduce latency
  • Auto-scaling and load balancing for peak traffic

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.