Citadel·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Citadel software engineering interview with a sliding window problem that had a twist I didn't fully see coming. The K-largest-per-window variant tripped me up more than I expected for what looked like a standard windowing question.

Questions Asked (1)

Given an integer array, a window size W, and an integer K, slide a window of size W across the array one step at a time and return the K largest elements (in descending order) for each window position.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just sort each window and slice off the top K, which the interviewer immediately flagged as too slow.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a solution using a balanced BST or a heap-based data structure to efficiently maintain the top K elements as the window slides. Discuss the trade-offs between different approaches, such as using a sorted container versus a heap, and analyze the time and space complexity.

Pro tip: Mention that you can optimize by using a data structure that supports efficient insertion, deletion, and retrieval of the K largest elements, such as a balanced BST or a skip list, and discuss how to handle duplicates and the order of output.

1. Clarify requirements and constraints

Ask about input size, value ranges, whether K can be larger than W, and if the output should be sorted descending. Confirm edge cases like empty array or W > array length.

2. Choose data structures

Decide on a data structure to maintain the window and extract top K efficiently. Consider a balanced BST (e.g., TreeSet in Java) or a max-heap with lazy deletion, and explain why.

3. Design the algorithm

Outline the steps: initialize the window with the first W elements, extract top K, then slide by removing the outgoing element and adding the incoming element, updating the data structure and extracting top K each time.

4. Analyze complexity and trade-offs

Discuss time complexity: O(N log W) with a balanced BST, or O(N log K) with a heap if optimized. Mention space complexity O(W) or O(K). Compare with naive O(N W log W) approach.

5. Handle edge cases and test

Walk through examples, including duplicates, K=1, K=W, and windows at boundaries. Ensure the solution returns correct descending order and handles all cases.

Key Points to Mention

Use of a balanced BST or a heap to maintain the window elements and efficiently retrieve the K largest.
Time complexity analysis: O(N log W) with a balanced BST, or O(N log K) with a heap if using a min-heap of size K.
Space complexity: O(W) for storing the window, or O(K) if only storing top K.
Handling duplicates: ensure the data structure allows duplicates or use a counter.
Edge cases: empty array, W > array length, K > W, and negative numbers.
Trade-offs between different data structures: e.g., sorted list vs. heap vs. balanced BST in terms of implementation complexity and performance.

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