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

Senior

Jun 2026

Summary

Google system design round for what felt like a mid-to-senior backend or full-stack role. One question, image upload and tagging for a travel site, but it opened up into a surprisingly broad conversation about storage, metadata, and scale.

Questions Asked (1)

Design a service that allows users to upload images and apply tags to them on a travel platform.

System DesignTechnical Trade-offsData Modeling

Author's notes

I started with the upload pipeline and spent way too long on chunked uploads before they nudged me toward the tagging side.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, such as scale, latency, and consistency needs. Then propose a high-level architecture covering image upload, storage, tagging, and retrieval, and dive into key components like data modeling and trade-offs. Finally, discuss scaling strategies and potential bottlenecks.

Pro tip: Emphasize the importance of separating image storage from metadata to optimize for cost and performance, and discuss how to handle eventual consistency in tag propagation. This shows you understand real-world trade-offs beyond just functional requirements.

1. Clarify Requirements

Ask questions to understand the scale (e.g., number of users, images per day), latency requirements, consistency needs, and whether tags are user-generated or system-generated. Also clarify if images need to be processed (e.g., resizing) and if there are privacy considerations.

2. High-Level Design

Outline the main components: a client application, an API gateway, an image upload service, a storage system (e.g., object storage for images, a database for metadata), a tagging service, and a search/retrieval service. Sketch the data flow from upload to tagging to retrieval.

3. Data Modeling

Design the schema for images and tags. Consider a relational database for metadata with tables for images, tags, and image-tag associations. Discuss indexing strategies for efficient tag-based queries and potential use of a search engine like Elasticsearch for flexible tag search.

4. Deep Dive into Key Components

Pick 1-2 areas to detail, such as the upload service (handling large files, chunked uploads, resumability) or the tagging service (how tags are applied, validated, and stored). Discuss trade-offs between synchronous vs. asynchronous tagging and consistency models.

5. Scaling and Trade-offs

Discuss how to scale each component: horizontal scaling of services, sharding the database, using CDNs for image delivery, and caching frequently accessed tags. Address trade-offs like consistency vs. availability, cost vs. performance, and complexity vs. maintainability.

Key Points to Mention

Separation of image storage (e.g., Google Cloud Storage) and metadata storage (e.g., Cloud SQL or Firestore) for scalability and cost efficiency.
Use of a message queue (e.g., Pub/Sub) for asynchronous processing of image uploads and tag indexing to decouple services and handle spikes.
Data model: many-to-many relationship between images and tags, with proper indexing for efficient tag-based queries.
Consideration of eventual consistency for tag updates and how it affects user experience; use of caching to mitigate.
Security and access control: authentication, authorization, and validation of uploaded images and tags to prevent abuse.
Monitoring and analytics: tracking upload success rates, tag usage, and system performance to inform scaling decisions.

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