Array Questions at DoorDash: What to Expect

Array questions dominate DoorDash’s technical interviews, making up 47 of their 87 most frequently asked problems. This isn’t a coincidence. DoorDash’s core business—matching drivers, orders, and restaurants in real-time—relies heavily on processing and optimizing lists of data. Whether it’s calculating the shortest delivery route, batching orders, or managing time windows, the underlying operations are often array manipulations. Mastering arrays is therefore non-negotiable for passing their coding screen and onsite rounds.

What to Expect — Types of Problems

DoorDash’s array questions are practical and map directly to their domain. You can expect three main categories:

1. Sliding Window & Two Pointers: These are paramount for problems involving contiguous subarrays, time-series data, or optimizing routes within a constraint (e.g., "Maximum Subarray," "Longest Substring Without Repeating Characters" applied to delivery IDs or time slots).
2. Sorting & Intervals: Scheduling is central to logistics. You’ll face problems about merging intervals (e.g., representing delivery windows) or scheduling tasks/orders with minimal conflict or delay.
3. Hash Map for Frequency & Pairs: Tracking counts of items, orders, or locations is common. Problems often involve finding pairs that sum to a target (like matching orders to drivers) or checking for duplicates.

The problems are rarely abstract; they are often wrapped in a light business context, but the core algorithm remains a standard array pattern.

How to Prepare — Study Tips with One Code Example

Focus on pattern recognition, not memorization. Practice until you can identify the underlying array technique within 30 seconds of reading a problem. For example, if a problem asks for the "longest" or "maximum" subarray meeting a condition (like staying under a maximum distance or time), think Sliding Window.

Here is a classic Sliding Window pattern for finding the length of the longest subarray with a sum less than or equal to a target k. This directly applies to scenarios like finding the longest route segment under a fuel limit.

def longest_subarray_sum_at_most_k(nums, k):
    left = 0
    current_sum = 0
    max_length = 0

    for right in range(len(nums)):
        current_sum += nums[right]
        # Shrink window from left if sum exceeds k
        while current_sum > k:
            current_sum -= nums[left]
            left += 1
        # Update max length for valid window
        max_length = max(max_length, right - left + 1)
    return max_length

Recommended Practice Order

1. Foundation: Start with basic two-pointer and prefix sum problems.
2. Core Patterns: Deep dive into Sliding Window (fixed and variable length) and Sorting/Intervals. These are the most frequent.
3. Optimization: Practice problems that combine arrays with hash maps for O(n) solutions.
4. DoorDash Context: Finally, solve the company-tagged problems on platforms like LeetCode to see how these patterns are applied to their specific scenarios.

Practice Array at DoorDash