Start by clarifying requirements and defining a robust input schema with validation, then design a modular pay calculation engine that applies configurable rules per delivery, handles edge cases like overlapping deliveries and missing fields, and aggregates results by day and dasher. Finally, outline a comprehensive test plan covering multiple pay configurations and rounding correctness.
Pro tip: Emphasize that you would separate the pay rule configuration from the calculation logic to allow easy updates and testing, and proactively discuss how you'd handle ambiguous cases like overlapping deliveries by clarifying with stakeholders or making reasonable assumptions.
Ask clarifying questions about pay rules, overlapping deliveries, and missing fields. Define a clear input schema for delivery events and pay rules, including data types and validation constraints.
Break down pay into components (base, distance, time, tips, bonuses) and design a function that computes pay per delivery based on configurable rules. Handle edge cases like overlapping deliveries by defining rules (e.g., sum time or take max) and missing fields with defaults or errors.
Group results by day and dasher ID, summing pay components. Apply rounding to cents at the appropriate stage (e.g., per delivery or final total) using a consistent method like round half up.
Create test cases covering at least three different pay configurations (e.g., different rates, bonuses, missing fields) and edge cases like overlapping deliveries and rounding. Use a testing framework to validate expected outputs.
Talk about trade-offs in design choices (e.g., per-delivery vs. per-shift rounding, handling overlaps) and how the solution could scale with large datasets or real-time processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.