Citadel·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

Apr 2026

Summary

Citadel software engineering interview with a system design problem centered on task scheduling. The question was more implementation-heavy than I expected for this kind of role, and the follow-up on deletion strategy is where things got interesting.

Questions Asked (1)

Design a task scheduler that supports inserting tasks with a priority, deleting a previously inserted task, and executing the next task in scheduling order. How would you handle all three operations efficiently?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I jumped straight to a min-heap for insert and execute, which was fine.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: single-threaded vs concurrent, priority range, and whether deletion is by task ID or handle. Then propose a hybrid data structure like a heap with a hash map for O(log n) insert/delete and O(1) lookup, and discuss trade-offs with alternatives like balanced BSTs or skip lists.

Pro tip: Mention that lazy deletion (marking tasks as deleted and skipping them during execution) can be simpler and faster in practice, but be ready to discuss its memory overhead and worst-case performance.

1. Clarify Requirements

Ask about concurrency, priority range, deletion semantics (by ID or handle), and performance expectations to tailor the solution.

2. Choose Core Data Structure

Propose a binary heap for efficient priority ordering, combined with a hash map for O(1) access to tasks by ID for deletion.

3. Handle Deletion Efficiently

Explain how to delete a task: either use indexed heap (store index in hash map) for O(log n) deletion, or lazy deletion with a 'deleted' flag.

4. Analyze Complexity and Trade-offs

Compare time/space complexity of your approach with alternatives (e.g., balanced BST, skip list) and discuss scenarios where each is preferable.

5. Address Concurrency and Scalability

If relevant, discuss thread-safety using locks or lock-free structures, and how to scale for high throughput.

Key Points to Mention

Time complexity: O(log n) for insert and delete, O(1) for peek/execute next.
Use of a hash map to map task IDs to heap indices for efficient deletion.
Lazy deletion as an alternative: mark as deleted and skip during execution, but discuss memory reclamation.
Comparison with balanced BST (e.g., TreeMap) which offers O(log n) for all operations and ordered traversal.
Handling duplicate priorities: stable ordering or tie-breaking by insertion time.
Concurrency considerations: locking, lock-free heaps, or actor model for thread safety.

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