Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

One coding round for a software engineer role at Snowflake. Pretty standard grid traversal problem but the follow-up discussion on algorithmic choices made it more interesting than I expected.

Questions Asked (1)

Given an m x n binary matrix of 0s and 1s, find the minimum distance between a start cell and a target cell, where you can only move through cells containing 0.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Went with BFS pretty much immediately since you want shortest path and DFS would've been a mess to reason about here.

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AI HintsAI Generated

Suggested Approach

Model the matrix as a graph where each 0-cell is a node connected to its 0-valued neighbors, then run BFS from the start to find the shortest path to the target. BFS guarantees the minimum distance in an unweighted grid, and you should discuss edge cases like unreachable targets or invalid start/target cells.

Pro tip: Clarify whether diagonal moves are allowed and whether the start/target cells must be 0; these assumptions drastically change the solution. Also, mention that bidirectional BFS can be more efficient for large grids with distant start and target.

1. Clarify problem constraints and assumptions

Ask about movement directions (4-way vs 8-way), whether start/target must be 0, and if the matrix can be modified. This ensures you solve the correct problem.

2. Choose BFS as the core algorithm

Explain that BFS is optimal for unweighted shortest path problems. Use a queue to explore level by level, marking visited cells to avoid cycles.

3. Handle edge cases and initialization

Check if start or target is blocked (1) or out of bounds; return -1 immediately if so. Initialize the queue with the start cell and a distance of 0.

4. Implement BFS traversal

While the queue is not empty, dequeue a cell, check if it's the target, and enqueue all valid unvisited 0-neighbors with distance+1. Return the distance when target is found.

5. Analyze complexity and discuss optimizations

State O(m*n) time and space complexity. Mention bidirectional BFS or A* with Manhattan distance heuristic as potential improvements for large grids.

Key Points to Mention

BFS guarantees shortest path in unweighted graphs, unlike DFS which may not find the minimum.
Use a visited set or modify the matrix in-place to avoid revisiting cells, ensuring O(m*n) time.
Edge cases: start or target is 1, out of bounds, or unreachable (return -1).
Space complexity can be reduced by using a queue of coordinates and marking visited directly in the matrix.
Bidirectional BFS can reduce search space by exploring from both start and target simultaneously.
A* with Manhattan distance heuristic can be more efficient if the grid is large and obstacles are sparse.

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