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

SeniorPrefer not to say

Jun 2026

Summary

System design round at Databricks for a software engineer role. The whole thing was one big question about building a cloud file sync service, and they really pushed on the details rather than letting you stay high-level.

Questions Asked (1)

Design a cloud file storage and sync service similar to Google Drive or Dropbox, covering upload and download mechanics, metadata management, versioning, conflict resolution, real-time multi-client sync, sharing and permissions, deduplication, scalability, and consistency.

System DesignTechnical Trade-offsData Modeling

Author's notes

This is a lot of surface area for one question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., number of users, file sizes, sync frequency), then design a high-level architecture covering storage, metadata, and sync components. Dive into key areas like chunking/deduplication, versioning, conflict resolution, and permissions, discussing trade-offs and scalability at each step.

Pro tip: Emphasize how you would leverage Databricks' strengths in data engineering and Delta Lake for metadata management and consistency, showing alignment with the company's tech stack. Also, proactively discuss failure scenarios and how the system recovers, demonstrating production maturity.

1. Clarify Requirements and Scale

Ask questions to understand expected user base, file sizes, sync latency, consistency needs, and sharing features. This sets the stage for design decisions.

2. High-Level Architecture

Outline core components: client apps, sync service, metadata store, block storage, and notification service. Explain data flow for upload, download, and sync.

3. Deep Dive into Key Mechanisms

Detail chunking, deduplication, versioning, conflict resolution (e.g., last-write-wins vs. operational transformation), and real-time sync using WebSockets or long polling.

4. Sharing, Permissions, and Security

Describe access control models (ACLs, RBAC), link sharing, encryption at rest and in transit, and how permissions are enforced across clients.

5. Scalability and Consistency Trade-offs

Discuss partitioning metadata, caching, eventual vs. strong consistency, and how to handle hotspots. Mention monitoring and failure recovery.

Key Points to Mention

Chunking and content-addressable storage for deduplication and efficient delta sync.
Metadata management using a scalable database (e.g., DynamoDB, Cassandra) with caching for low-latency lookups.
Versioning via immutable chunks and a version history table; conflict resolution strategies like vector clocks or CRDTs.
Real-time sync using push notifications (WebSockets, long polling) and change logs.
Sharing and permissions with fine-grained ACLs and efficient permission checks.
Scalability through horizontal partitioning, consistent hashing, and CDN for downloads.

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