The part that tripped me up first was realizing I had to drive the API shape conversation before writing a single line of code.
Start by clarifying the problem scope and defining a clear input/output contract with the interviewer, then propose a state-machine-based algorithm that processes events chronologically and applies pay rules. Focus on handling edge cases like overlapping events, missing data, and time zone consistency while keeping the solution extensible.
Pro tip: Explicitly state your assumptions about event ordering and data completeness, and suggest a validation layer to catch inconsistencies—this shows you think about real-world data quality and production readiness.
Ask questions to understand the event types, pay rules, and expected output format. Propose a concrete input schema (e.g., list of events with timestamps and types) and output schema (e.g., total pay breakdown).
Outline a state machine that processes events in chronological order, tracking order state and accumulating pay components (base, distance, time, peak, tips). Explain how to handle state transitions and calculations.
Discuss handling of missing events, out-of-order timestamps, overlapping wait periods, and time zone normalization. Mention validation and error handling strategies.
State the time and space complexity (e.g., O(n) for n events) and discuss trade-offs between simplicity and extensibility, such as hardcoding rules vs. configurable pay rules.
Propose test cases covering normal flow, edge cases (e.g., no tips, multiple waits), and invalid data. Suggest unit tests for each pay component and integration tests for the full pipeline.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.