← Aurora Interview Insights

Aurora·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Interviewed for a Software Engineer role at Aurora and got hit with a sliding window minimum problem that had a streaming constraint baked in. Clean problem on the surface, but the O(n) requirement is where things get interesting.

Questions Asked (1)

Q1

Given an integer array and a window size k, implement a streaming algorithm that outputs the minimum value of the most recent k elements at each index in O(n) time.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The streaming angle is what tripped me up at first.

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AI HintsAI Generated

Suggested Approach

Use a monotonic deque to maintain indices of elements in the current window, ensuring the front always holds the minimum. Process each element once, adding to the back and removing from the front when out of window, achieving O(n) time and O(k) space.

Pro tip: Mention that the deque stores indices, not values, to easily check if the front is out of the window. Also, note that the algorithm handles streaming data naturally, making it suitable for real-time applications.

1. Clarify requirements and edge cases

Confirm window size k, array size n, and handle cases like k > n, empty array, or k=1. Discuss expected output format (e.g., array of minima).

2. Choose data structure

Select a monotonic deque (double-ended queue) to efficiently track the minimum. Explain why it outperforms naive O(nk) or heap-based O(n log k) approaches.

3. Describe algorithm steps

Iterate through the array: remove indices from the back while the current element is smaller, add current index, remove front if out of window, and output front value when window is full.

4. Analyze complexity

State that each element is added and removed at most once, giving O(n) time and O(k) space. Compare with alternative approaches.

5. Discuss trade-offs and extensions

Mention trade-offs like memory vs. time, and potential extensions for streaming data or multiple windows. Highlight suitability for real-time systems.

Key Points to Mention

  • Monotonic deque maintains elements in increasing order, so front is always the minimum.
  • Store indices instead of values to easily check if the front is outside the current window.
  • Each element is processed once, resulting in O(n) time complexity.
  • Space complexity is O(k) for the deque, which is optimal for this problem.
  • The algorithm naturally handles streaming data, making it suitable for real-time applications.
  • Edge cases: k=1, k > n, empty array, and negative numbers.

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