I went straight into the data model and storage layer, which felt safe, but about 15 minutes in they nudged me toward the actual pricing engine.
Start by clarifying the business context and requirements, such as what products are priced, how often prices change, and what constraints exist (e.g., competitor prices, inventory, profit margins). Then outline a high-level architecture that separates concerns: data ingestion, pricing engine, and price serving, while discussing trade-offs between real-time and batch processing. Finally, dive into key components like data pipelines, pricing algorithms, scalability, and monitoring.
Pro tip: Emphasize the importance of A/B testing and gradual rollouts to validate pricing changes, as pricing directly impacts revenue and customer trust. Also, mention how you would handle failures gracefully, such as falling back to static prices if the dynamic system fails.
Ask questions to understand the business goals, scale, data sources, and constraints. Determine if pricing is for e-commerce, ride-sharing, etc., and what factors influence price (demand, competition, inventory).
Sketch a system diagram with components: data collection (e.g., competitor prices, demand signals), pricing engine (rule-based or ML models), and price delivery (APIs, caching). Discuss batch vs. real-time processing.
Elaborate on the pricing engine: how to compute prices using algorithms (e.g., regression, reinforcement learning), handle constraints (min/max prices), and ensure low latency. Discuss data storage and processing frameworks.
Explain how to scale to millions of products and requests per second, using techniques like sharding, caching, and asynchronous processing. Address fault tolerance and fallback mechanisms.
Describe how to monitor system performance and business metrics (e.g., conversion rate, revenue). Discuss A/B testing, logging, and alerting for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.