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DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

DoorDash software engineering interview that centered on a practical coding problem tied directly to their core business. The follow-up pushed into product-adjacent territory which I wasn't fully expecting from a coding round.

Questions Asked (2)

Q1

Write a function that calculates a delivery driver's total daily pay from a list of delivery records, where each record might include start time, miles driven, a base rate, and a tip. Handle missing or invalid fields gracefully, apply a minimum pay floor per delivery, and return a per-day total.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The error handling part is where I spent most of my mental energy.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a robust function that validates each record, computes pay as max(base + tip, minimum floor), and sums per day. Emphasize clean code, error handling, and testability.

Pro tip: Mention that you would log or flag invalid records for monitoring rather than silently dropping them, and discuss how you'd handle timezone or date boundaries for daily aggregation.

1. Clarify Requirements and Edge Cases

Ask about the data format, what constitutes an invalid record, the minimum pay floor value, and how to handle missing fields. Confirm whether records are for a single day or multiple days.

2. Design the Function Signature and Data Validation

Define the function to accept a list of delivery records and return a total. For each record, validate required fields (e.g., base rate, tip) and skip or handle invalid ones gracefully.

3. Compute Per-Delivery Pay with Minimum Floor

For each valid record, calculate pay as base rate + tip, then apply the minimum pay floor using max(pay, floor). Ensure numeric types are handled correctly.

4. Aggregate and Return Daily Total

Sum the per-delivery pay for all valid records and return the total. If records span multiple days, group by date and return a per-day total (e.g., a dictionary).

5. Test and Discuss Trade-offs

Walk through test cases: empty list, all invalid records, missing fields, negative values, and records that hit the floor. Discuss trade-offs like strict vs. lenient validation and performance.

Key Points to Mention

  • Input validation and graceful handling of missing or invalid fields (e.g., using defaults or skipping records).
  • Application of a minimum pay floor per delivery using max(base + tip, floor).
  • Aggregation logic for daily totals, including grouping by date if multiple days are present.
  • Edge cases: empty list, null values, negative numbers, and records that trigger the floor.
  • Time complexity: O(n) for n records, and space complexity for storing per-day totals.
  • Trade-offs between strict validation (fail fast) and lenient validation (skip invalid records) with logging.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Extend your solution to support a configurable 'peak hour' rule, where deliveries starting within certain time windows earn a higher pay rate. The time windows should be passed in as a parameter.

Algorithms & Data StructuresSystem Design
Author's notes

Didn't see this coming as a follow-up.

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AI HintsAI Generated

Suggested Approach

First, clarify the input format for the peak hour windows and how they map to pay rates. Then, modify the existing solution to check if each delivery's start time falls within any peak window and apply the corresponding higher rate. Ensure the solution remains efficient and handles edge cases like overlapping windows or boundary times.

Pro tip: Discuss how you would handle overlapping peak windows—either by merging them or by defining precedence rules—and mention that you would confirm this with the interviewer to avoid ambiguity.

1. Clarify requirements

Ask questions to understand the exact format of the peak hour windows (e.g., list of start/end times, associated rates) and how they should be applied (e.g., highest rate wins, first match wins).

2. Design data structure

Choose an appropriate data structure to store the peak windows, such as an array of intervals or a sorted list, and consider if preprocessing (e.g., merging overlapping intervals) is needed.

3. Modify core logic

Integrate a check for each delivery: if its start time falls within any peak window, use the higher pay rate; otherwise, use the base rate. Ensure the check is efficient, possibly using binary search if intervals are sorted.

4. Handle edge cases

Consider boundary conditions (inclusive/exclusive), overlapping windows, and empty peak windows. Discuss how to resolve conflicts (e.g., take the maximum rate).

5. Analyze complexity

Explain the time and space complexity of your solution, and discuss potential optimizations if the number of deliveries or peak windows is large.

Key Points to Mention

  • Input format for peak windows: list of tuples (start, end, rate) or similar.
  • Handling overlapping windows: merge intervals or define precedence (e.g., highest rate).
  • Efficiency: O(log n) lookup with binary search if intervals are sorted, or O(n) with linear scan.
  • Edge cases: deliveries exactly at window boundaries, empty peak windows, multiple windows.
  • Extensibility: how to add more rules (e.g., day-of-week specific rates) in the future.
  • Testing: unit tests for various scenarios including no peak, single peak, overlapping peaks.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.