I jumped straight into data models and GPS tracking without stopping to clarify who the users actually are.
Start by clarifying functional and non-functional requirements, then estimate scale (users, rides, data volume) to drive design decisions. Propose a high-level architecture covering data ingestion, storage, processing, and serving, and dive into critical components like real-time metrics and scalability. Discuss trade-offs and how you would measure success with product metrics.
Pro tip: At Amazon, always tie technical decisions back to customer impact and business metrics—show how your design improves the cyclist experience and supports Amazon's flywheel. Also, proactively discuss operational excellence (monitoring, deployment, cost) to demonstrate ownership.
Ask questions to understand the product scope: what metrics to track (speed, distance, heart rate, power), target users (casual vs. competitive), and key features (real-time tracking, social, training plans). Define non-functional requirements like scalability, latency, and availability.
Estimate the number of users, rides per day, data points per ride, and storage/throughput needs. This informs technology choices and highlights bottlenecks.
Outline the end-to-end system: mobile app for data collection, ingestion pipeline (e.g., Kinesis), storage (e.g., S3 for raw, DynamoDB for metadata, time-series DB for metrics), processing (Lambda, Flink), and APIs for serving data to clients.
Pick 1-2 critical areas to detail, such as real-time metric computation, handling late/out-of-order data, or designing the data model for efficient queries. Discuss trade-offs (e.g., Lambda vs. EC2, SQL vs. NoSQL).
Define product metrics (DAU, ride completion rate, user engagement) and system metrics (latency, error rates). Explain how to monitor, alert, and iterate on the design based on feedback and scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.