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Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Snowflake software engineer interview with a graph traversal problem that also required building a small simulator on top of the core solution. Pretty standard algorithmic round but the added simulator layer made it feel more applied than usual.

Questions Asked (1)

Q1

Given a network of wiki pages where each page links to other pages, find the minimum number of clicks to navigate from a start page to a target page. You also need to implement a simulator that, given a page, returns all the links on it.

Algorithms & Data StructuresSystem Design
Author's notes

BFS was the right call and I knew it pretty quickly.

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AI HintsAI Generated

Suggested Approach

Model the wiki pages as a graph where nodes are pages and directed edges are links. Use BFS from the start page to find the shortest path to the target, leveraging the simulator to fetch links. Discuss complexity and potential optimizations for large-scale graphs.

Pro tip: Clarify assumptions upfront: whether the graph is unweighted (each click costs 1), if links are directed, and if the target is guaranteed reachable. Also, mention that bidirectional BFS can significantly reduce search space in practice.

1. Clarify requirements and assumptions

Confirm that each click has uniform cost, links are directed, and the graph may be large or have cycles. Ask if the simulator can be called multiple times or if caching is allowed.

2. Model as a graph problem

Represent pages as nodes and links as directed edges. The problem reduces to finding the shortest path in an unweighted directed graph.

3. Choose BFS for shortest path

Explain that BFS explores level by level, guaranteeing the minimum number of clicks. Use a queue and a visited set to avoid cycles.

4. Implement the algorithm with the simulator

Initialize queue with start page, mark visited, and iterate: dequeue, if target return distance, else fetch links via simulator, enqueue unvisited neighbors with distance+1.

5. Analyze complexity and optimizations

Time O(V+E), space O(V). For large graphs, consider bidirectional BFS, early termination, or distributed processing. Mention caching simulator results if repeated queries.

Key Points to Mention

  • BFS guarantees shortest path in unweighted graphs
  • Use a visited set to avoid infinite loops due to cycles
  • Time and space complexity: O(V+E) and O(V) respectively
  • Bidirectional BFS can reduce search space from O(b^d) to O(b^(d/2))
  • Handle edge cases: start equals target, target unreachable, empty graph
  • Caching simulator results can improve performance for multiple queries

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