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

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Jul 2026

Summary

Snapchat system design round focused entirely on ad infrastructure, specifically how you'd track impressions and spend at scale and then serve that aggregated data back to the ad server fast enough to be useful. Pretty deep dive, more backend-heavy than I expected for an ads-adjacent role.

Questions Asked (1)

Design a system that tracks ad impressions and ad spend at scale, and serves aggregated data back to the ad server with low latency, given that each user request involves looking up data for roughly 10,000 candidate ads.

System DesignTechnical Trade-offsData Modeling

Author's notes

The 10k candidate ads per request detail is what made this tricky.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a hybrid architecture that separates the write path (ingestion and aggregation) from the read path (low-latency serving). Focus on pre-aggregation, caching, and data modeling to handle the 10,000-ad lookup efficiently.

Pro tip: Emphasize that the ad server should not query raw data; instead, pre-aggregate and cache results, and consider approximate algorithms for real-time counters to balance accuracy and latency.

1. Clarify Requirements and Scale

Ask about data volume, latency SLA, consistency needs, and query patterns to scope the problem. Confirm that the ad server needs aggregated metrics (e.g., impressions, spend) for 10,000 ads per request.

2. Design Data Ingestion and Aggregation Pipeline

Propose a scalable ingestion layer (e.g., Kafka) to collect ad events, and a stream processing layer (e.g., Flink, Spark Streaming) to compute aggregates in real-time. Store aggregates in a low-latency store (e.g., Redis, Cassandra).

3. Optimize Read Path for Low Latency

Implement a multi-level caching strategy (in-memory, distributed cache) and pre-compute aggregates for frequent queries. Use batch fetching or pipelining to retrieve data for 10,000 ads efficiently.

4. Address Data Modeling and Storage Trade-offs

Choose appropriate data models (e.g., time-series, key-value) and storage engines based on access patterns. Discuss partitioning, replication, and consistency trade-offs.

5. Discuss Scalability, Fault Tolerance, and Monitoring

Explain how the system scales horizontally, handles failures, and ensures data accuracy. Mention monitoring, alerting, and backfill strategies.

Key Points to Mention

Use of stream processing for real-time aggregation (e.g., Kafka + Flink)
Multi-level caching (local + distributed) to reduce latency
Data modeling: time-series or key-value stores for aggregates
Approximate algorithms (e.g., HyperLogLog, Count-Min Sketch) for high-cardinality counts
Batch fetching and pipelining for 10,000 ad lookups
Trade-offs between consistency, latency, and cost

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