Netflix·Software Engineer·Online Assessment (OA)·Intermediate

IntermediatePass

Jun 2026Remote

Summary

Netflix SWE OA, pretty standard stuff. The interviewer was easy to talk to and actually tailored the problem to my background, which made it feel less like a test and more like a working session. Coded slower than I'd like but got through it, and had a VO invite two days later.

Questions Asked (1)

Design and implement a versioned key-value store, framed around operations common in an ads data platform.

System DesignData ModelingTechnical Trade-offs

Author's notes

The interviewer noticed I work in ads and suggested we reframe the problem around that context, which was a nice touch.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., read/write ratio, latency, consistency, versioning semantics) for an ads data platform. Then propose a high-level design (e.g., LSM-tree based storage with versioning via timestamps or version numbers) and dive into data modeling, API, and trade-offs. Finally, discuss implementation details like compaction, indexing, and how to handle concurrent updates and reads.

Pro tip: Emphasize how versioning enables time-travel queries and auditability, which are critical for ads attribution and billing. Also, discuss how you'd handle hot keys and write amplification, common in ad tech workloads.

1. Clarify Requirements and Scale

Ask about expected QPS, data size, read/write patterns, latency SLAs, consistency needs, and versioning semantics (e.g., per-key version history, TTL).

2. High-Level Design

Propose a distributed key-value store architecture (e.g., sharded, replicated) with a storage engine that supports versioning, such as LSM-trees with versioned keys or a B-tree with version chains.

3. Data Model and API

Define the key schema (e.g., key + version) and operations: put(key, value), get(key, version?), delete(key, version). Explain how versions are assigned (e.g., timestamp, monotonic counter).

4. Implementation Details

Discuss storage layout, indexing (e.g., sparse index, bloom filters), compaction strategies, and how to handle concurrent writes and reads (e.g., MVCC, snapshots).

5. Trade-offs and Optimizations

Compare design choices (e.g., LSM vs B-tree, strong vs eventual consistency) and propose optimizations for ads use cases like caching hot keys, batch writes, and tiered storage.

Key Points to Mention

Versioning semantics: how to assign versions (timestamps, version numbers) and support point-in-time reads.
Storage engine choice: LSM-trees for write-heavy workloads vs B-trees for read-heavy, and how versioning affects compaction.
Consistency and replication: trade-offs between strong and eventual consistency, and how to handle conflicts in a distributed setting.
Scalability: sharding strategies (e.g., consistent hashing) and replication for fault tolerance.
Performance optimizations: caching, bloom filters, and handling hot keys common in ad tech.
Use cases in ads: attribution, billing, and real-time bidding requiring low-latency reads and versioned data.

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