This one tripped me up more than it should have.
Start with a high-level overview of your system's scale, then dive into specifics like dataset sizes, throughput, storage, and processing. Highlight a scaling problem you faced, the trade-offs you considered, and the impact of your solution. Tailor your answer to emphasize real-time, user-facing systems similar to Roblox's environment.
Pro tip: Quantify scale with concrete numbers (e.g., '10M DAU, 1PB data, 100K QPS') and focus on the 'why' behind architectural decisions, not just the 'what'. Show how you balanced consistency, latency, and cost.
Briefly describe your current team's product, its purpose, and your role to ground the scale discussion.
Provide specific metrics: dataset sizes (TB/PB), daily active users, requests per second, data ingestion rates, and storage growth.
Explain how data is stored (e.g., SQL/NoSQL, data lakes) and processed (batch vs. stream, frameworks like Spark, Flink), including key components.
Detail a significant scaling problem, its symptoms, root cause, and the trade-offs you evaluated (e.g., consistency vs. availability, cost vs. performance).
Explain the solution implemented, its impact (e.g., reduced latency, cost savings), and what you learned about designing scalable systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.