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

Senior
Apr 2026

Summary

Amazon system design round, one question about building a pipeline that pulls book reviews from Amazon and feeds recommendations back to some external site. Pretty open-ended, took me a while to figure out where to even start.

Questions Asked (1)

Q1

Design a system that ingests book reviews from Amazon and uses them to serve personalized book recommendations on an external website.

System DesignTechnical Trade-offsData Modeling
Author's notes

I started with the ingestion side and spent probably too long on the data pipeline before touching the recommendation layer.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design a high-level architecture that separates data ingestion, storage, recommendation computation, and serving. Focus on data modeling for reviews and user interactions, and discuss trade-offs between batch and real-time processing for personalization.

Pro tip: Emphasize how you would leverage Amazon's existing infrastructure (e.g., S3, Kinesis, SageMaker) to avoid reinventing the wheel, and discuss how to handle cold-start users and privacy concerns.

1. Clarify Requirements and Scale

Ask about expected data volume, latency requirements, personalization depth, and whether recommendations need to be real-time or can be batch-updated. This sets the stage for architectural decisions.

2. Design Data Ingestion Pipeline

Outline how to ingest reviews from Amazon (e.g., via APIs, web scraping, or internal data streams) and store them in a scalable data lake (e.g., S3) and a database for structured access (e.g., DynamoDB).

3. Model Data and User Interactions

Define schemas for reviews, books, and user behavior (e.g., clicks, purchases). Discuss how to capture implicit and explicit feedback for personalization.

4. Build Recommendation Engine

Choose algorithms (collaborative filtering, content-based, or hybrid) and design the computation layer (batch with Spark or real-time with streaming). Explain how to train and update models.

5. Serve Recommendations and Handle Trade-offs

Design an API to serve recommendations with low latency, caching, and fallback strategies. Discuss trade-offs like consistency vs. availability, cost, and privacy.

Key Points to Mention

  • Scalability: use distributed systems like Kafka/Kinesis for ingestion, S3 for storage, and Spark/Flink for processing.
  • Data modeling: star schema for reviews, user-item interaction matrix, and feature store for ML.
  • Recommendation algorithms: collaborative filtering (ALS), content-based, and hybrid approaches; handle cold start with popularity or demographic-based recommendations.
  • Batch vs. real-time: trade-offs between freshness and cost/complexity; lambda architecture for combining both.
  • Serving layer: low-latency API with caching (Redis), CDN for static assets, and A/B testing for model evaluation.
  • Privacy and security: anonymize user data, comply with GDPR/CCPA, and secure data pipelines.

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