Clarify the pay model and event semantics first, then design a state machine that processes events in timestamp order, handling out-of-order and missing events via sorting and validation. Compute pay per delivery and apply the shift minimum guarantee, discussing trade-offs between batch and streaming processing.
Pro tip: Explicitly discuss how you would handle out-of-order events (e.g., using a priority queue or sorting by timestamp) and missing events (e.g., inferring from context or flagging for review), as this demonstrates production-level thinking.
Ask about the exact pay formula, event ordering guarantees, and what constitutes a missing event. Confirm whether events can be duplicated or arrive late.
Define a state machine for deliveries (e.g., offered -> accepted -> picked up -> dropped off) and choose data structures to track active deliveries and shift totals.
Sort events by timestamp or use a priority queue to process in order. For missing events, decide whether to infer (e.g., assume dropoff if pickup exists) or skip and log.
Calculate base, mileage, time, and tips per delivery, sum them, and compare with the shift minimum guarantee to determine final pay.
Talk about batch vs. streaming, memory vs. accuracy, and how to handle cancellations, partial data, and timezone issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.