Choose one or two concrete projects where you built or significantly improved a data collection and aggregation system, and narrate them using a clear structure: context, requirements, design decisions, trade-offs, and outcomes. Focus on the engineering challenges—such as scale, reliability, and data modeling—and how you solved them, rather than just listing technologies.
Pro tip: Quantify the impact of your system (e.g., throughput, latency, cost savings, data volume) and explicitly discuss trade-offs you made, such as consistency vs. availability or batch vs. streaming, to show senior-level judgment.
Briefly describe the product or business need, the scale of data (volume, velocity, variety), and the key requirements like latency, accuracy, and cost constraints.
Walk through the high-level architecture: data sources, ingestion methods (e.g., batch, streaming, CDC), storage layers, processing engines, and aggregation/serving layers.
Discuss specific choices you made—such as schema design, partitioning, consistency models, or technology selection—and why you chose them over alternatives.
Describe obstacles like data skew, late-arriving data, scaling bottlenecks, or failures, and the solutions you implemented (e.g., backpressure, idempotency, retries).
Quantify the results (e.g., reduced latency by X%, handled Y events per second) and reflect on what you would do differently or how you evolved the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.