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

Senior

Jun 2026

Summary

System design round at Tradedesk for a software engineer role. One meaty question about designing a pipeline around some pretty demanding constraints, and from what I can tell it's a question they reuse a lot.

Questions Asked (1)

Design a data pipeline that handles roughly 30GB of data and supports around 4 million read-heavy requests per second.

System DesignTechnical Trade-offsData Modeling

Author's notes

This one is clearly a repeat question for them, which is both reassuring and kind of stressful because it means they know exactly what a good answer looks like.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data characteristics and access patterns, then design a read-optimized pipeline that precomputes and caches aggressively. Focus on horizontal scaling with partitioning, replication, and a multi-tier caching strategy to handle 4M read-heavy requests per second.

Pro tip: Emphasize that 30GB is small enough to fit in memory across a cluster, so you can prioritize in-memory caching and denormalization over complex disk-based storage. Also, discuss the trade-offs between consistency and latency, and how you'd monitor and adapt the system.

1. Clarify Requirements and Assumptions

Ask questions to understand data sources, update frequency, read/write ratio, latency SLAs, and query patterns. Confirm that the workload is read-heavy and identify key access patterns.

2. Design Data Ingestion and Processing

Outline how data is ingested (batch or stream), transformed, and stored. Since data is only 30GB, consider in-memory processing and precomputation of views to serve reads efficiently.

3. Design Storage and Serving Layer

Choose a storage solution optimized for reads, such as an in-memory database or a distributed cache with persistence. Partition and replicate data to scale horizontally and ensure high availability.

4. Implement Caching and CDN

Introduce multiple cache layers (client, CDN, application, database) to absorb the 4M RPS. Use cache invalidation strategies and consider edge caching for global low-latency access.

5. Address Scalability, Consistency, and Monitoring

Discuss how to scale the system (e.g., adding nodes), trade-offs between consistency and availability, and how to monitor performance and handle failures.

Key Points to Mention

Data partitioning and replication strategies (e.g., sharding by key, consistent hashing)
Caching layers and eviction policies (e.g., Redis, Memcached, LRU)
Read-heavy optimization techniques (e.g., denormalization, materialized views, read replicas)
Latency and throughput considerations (e.g., batching, async I/O, connection pooling)
Trade-offs between consistency models (strong vs. eventual) and their impact on read performance
Monitoring and auto-scaling to handle traffic spikes and ensure reliability

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