Snowflake·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Snowflake SWE interview with a graph/topological sort problem that had a twist I wasn't fully prepared for. The core question was straightforward but the follow-up about grouping tasks by levels added enough complexity to make me sweat.

Questions Asked (1)

Given N tasks labeled 0 to N-1 and a list of prerequisite pairs, return a valid execution order of all tasks (or an empty list if none exists). Also group the tasks by levels, where each level contains tasks that can only begin once all tasks in earlier levels have finished. Walk through your algorithm, its complexity, and how you handle cycle detection.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew Kahn's algorithm well enough to get the basic ordering out, but the level-grouping part tripped me up for a minute.

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AI HintsAI Generated

Suggested Approach

Model the tasks and prerequisites as a directed graph, then use topological sorting (Kahn's algorithm) to produce a valid order and detect cycles. To group tasks by levels, process nodes in BFS layers, where each layer contains nodes with zero in-degree after removing previous layers. Explain the algorithm step-by-step, analyze time and space complexity, and discuss how cycle detection falls out naturally when not all nodes are processed.

Pro tip: Snowflake values scalable, production-ready solutions, so mention how your approach handles large graphs (e.g., using adjacency lists and iterative BFS to avoid recursion limits) and how you would validate the output (e.g., check that all dependencies are satisfied).

1. Clarify and Model

Confirm input format (e.g., N tasks, list of prerequisite pairs) and edge cases (empty input, disconnected components). Model the problem as a directed graph where an edge u→v means u must precede v.

2. Choose Algorithm

Select Kahn's algorithm (BFS-based topological sort) for its simplicity and natural cycle detection. Alternatively, mention DFS-based topological sort but note its recursion depth risk.

3. Compute In-Degrees and Initialize Queue

Calculate in-degree for each node, enqueue all nodes with in-degree 0. These are the tasks that can start immediately (level 0).

4. Process Levels and Build Order

While the queue is not empty, process all nodes in the current level: add them to the order, decrement in-degrees of their neighbors, and enqueue neighbors that reach in-degree 0. Record each level as a list.

5. Detect Cycles and Analyze Complexity

If the number of processed nodes is less than N, a cycle exists; return an empty list. Otherwise, return the order and levels. State time complexity O(V+E) and space O(V+E).

Key Points to Mention

Topological sorting using Kahn's algorithm (BFS) vs. DFS-based approach
Level grouping via BFS layers: nodes with zero in-degree at each iteration
Cycle detection: if processed nodes < N, a cycle exists
Time and space complexity: O(V+E) time, O(V+E) space
Handling edge cases: empty graph, disconnected components, self-loops
Scalability considerations: adjacency list representation, iterative BFS to avoid recursion limits

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