Google Backend Engineer Interview Questions

I would use a durable streaming layer so producers can write events quickly without waiting for downstream consumers. The system should partition events by a stable key, batch writes where possible, and allow multiple consumers to process different partitions in parallel. The reason I choose streaming ingestion is that it scales much better than trying to write everything directly into a database. I would also think about backpressure, replay, and schema evolution because click streams tend to grow quickly over time. If the interviewer asks, I would explain how I would move from raw events to aggregates.

I would think about a trie first, because prefix search is the core strength of that structure. If the data set is huge, I would also think about a compact prefix index or an inverted structure that balances speed and memory use. The reason I choose a trie is that it gives very fast lookup for prefix-based queries, which is exactly what the feature needs. I would also mention that if ranking is important, I would store popularity or scoring metadata at each node. The trade-off is memory usage, since tries can become large if they are not compressed.

I would make the consumer idempotent and store enough state to know whether a message has already been handled. That could mean tracking processed IDs, using transactional writes, or writing a status record before acknowledging the message. The reason I choose this direction is that retries are normal and the system should behave correctly even when the same message appears again. I would also think about how long to keep deduplication state and how to recover from partial failure. If the interviewer wants more depth, I would talk about exactly-once versus at-least-once semantics.

I would first clarify what 'nearest' means, because it could mean lowest network latency, same country, or lowest cost. My design would likely use geo-aware routing with health checks so traffic goes to a nearby healthy region. The reason I choose this approach is that user experience usually improves when requests do not travel farther than necessary. I would also think about failover if the closest region is unhealthy, because latency is useless if the request cannot be served. At scale, I would mention the need for consistent routing behavior and careful monitoring.

I would first identify the bottleneck rather than guessing. The main things I would look at are statelessness, database pressure, caching, connection limits, and whether the service can be horizontally scaled cleanly. The reason I choose to start there is that concurrency issues usually come from shared resources, not from the request handler itself. I would also think about load shedding and graceful degradation if traffic exceeds capacity. If the interviewer wants a concrete example, I would say I would first make the service stateless, then protect the database, then add caching and queueing where needed.