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Square·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Square coding interview, graph traversal problem built on top of a transaction tracking system they had you extend. Pretty clean problem once you saw it for what it was.

Questions Asked (1)

Q1

Given an existing customer transaction tracker, implement a method that takes a customer and returns all customers reachable through any chain of transactions, essentially finding the connected component for that customer in the transaction graph.

Algorithms & Data StructuresSystem Design
Author's notes

Took me a second to see it as a graph problem.

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AI HintsAI Generated

Suggested Approach

Clarify the graph representation (adjacency list vs. matrix) and whether transactions are directed or undirected. Then implement a traversal (DFS or BFS) from the given customer, tracking visited nodes to avoid cycles, and return the set of reachable customers. Discuss trade-offs and edge cases.

Pro tip: Mention that in a production system at Square, you'd likely use a union-find data structure for dynamic connectivity or precompute connected components for performance, but for a single query, traversal is simpler and sufficient.

1. Clarify requirements and assumptions

Ask whether the transaction graph is directed or undirected, whether it's static or dynamic, and what the expected scale is. Confirm that 'reachable' means via any chain of transactions, implying undirected connectivity.

2. Choose data structures and algorithm

Represent the graph as an adjacency list for efficient traversal. Select DFS or BFS to explore all connected nodes, using a visited set to prevent infinite loops in cyclic graphs.

3. Implement the traversal

Write a function that starts from the given customer, explores all neighbors recursively (DFS) or iteratively (BFS), and collects all visited customers. Ensure the starting customer is included in the result.

4. Analyze complexity and edge cases

State time and space complexity: O(V + E) for traversal, O(V) for visited set. Discuss edge cases: customer not in graph, isolated customer, large graph, and cycles.

5. Discuss optimizations and alternatives

Mention union-find for frequent queries or dynamic updates, or precomputing connected components if the graph is static. Also note trade-offs between DFS (recursion depth) and BFS (memory).

Key Points to Mention

  • Graph representation: adjacency list vs. adjacency matrix, and why adjacency list is preferred for sparse graphs.
  • Traversal choice: DFS vs. BFS, including iterative vs. recursive implementations and their trade-offs.
  • Cycle handling: using a visited set to avoid infinite loops and ensure each node is processed once.
  • Complexity analysis: O(V + E) time and O(V) space for traversal.
  • Edge cases: customer not present, isolated node, self-loops, and large-scale graphs.
  • Alternative approaches: union-find for dynamic connectivity or precomputed components for read-heavy workloads.

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