DoorDash·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

DoorDash analytics engineer interview, got a coding question that was pretty clearly a sliding window problem once I sat with it for a minute. Nothing crazy but you do need to actually know your O(n) approaches cold.

Questions Asked (1)

Given an integer array and an integer k, find the maximum sum of any contiguous subarray with exactly k elements. Values can be negative. Expected time complexity is O(n).

Algorithms & Data Structures

Author's notes

My first instinct was brute force, which I'm pretty sure they clocked immediately.

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AI HintsAI Generated

Suggested Approach

Use a sliding window of fixed size k to compute the sum of the first k elements, then slide the window by adding the next element and subtracting the element leaving the window. Track the maximum sum seen. This achieves O(n) time and O(1) space.

Pro tip: Clarify that the window size is fixed at k, so we don't need to expand/shrink like in variable-size sliding window problems. Also, mention that initializing max_sum to negative infinity handles all-negative arrays correctly.

1. Understand the problem

Confirm that we need the maximum sum of a contiguous subarray of exactly k elements, and that the array can contain negative numbers. Note the O(n) time requirement.

2. Choose the sliding window technique

Since the window size is fixed, use a sliding window approach. Compute the sum of the first k elements as the initial window.

3. Slide the window

Iterate from index k to n-1, updating the window sum by adding the new element and subtracting the element that falls out of the window (at index i-k).

4. Track the maximum

After each slide, compare the current window sum with the maximum sum found so far and update if larger.

5. Return the result

After processing all windows, return the maximum sum. Handle edge cases like k > n by returning 0 or throwing an error, depending on requirements.

Key Points to Mention

Time complexity: O(n) because we traverse the array once.
Space complexity: O(1) as we only use a few variables.
Sliding window is optimal for fixed-size subarray problems.
Initializing max_sum to negative infinity to handle all-negative arrays.
Edge cases: k = 0, k > array length, empty array.
The difference between fixed-size and variable-size sliding windows.

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