Stripe·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Stripe coding interview that revolves around a graph/union-find problem disguised as a data deduplication task. The problem sounds like a simple similarity check but the transitive linking requirement is what actually makes it interesting.

Questions Asked (1)

Given a list of user records with fields like id, name, email, and company, along with per-field similarity weights and a threshold, find all records transitively linked to a target user. Two records are linked if their weighted similarity score meets the threshold, and you need to return the entire connected component containing the target with no hop limit.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with BFS which felt natural, but the interviewer kept pushing on what happens when n gets large.

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AI HintsAI Generated

Suggested Approach

Model the problem as a graph where each record is a node and edges exist between records whose weighted similarity meets the threshold. Then perform a graph traversal (BFS/DFS) from the target node to find its connected component, discussing efficiency and scalability trade-offs.

Pro tip: Mention that computing all pairwise similarities is O(n²) and can be a bottleneck; propose blocking or indexing techniques to reduce comparisons, and clarify whether the graph is static or dynamic.

1. Clarify requirements and constraints

Ask about data size, whether similarities are precomputed, and if the graph is static or dynamic. Confirm the definition of weighted similarity and threshold.

2. Model as a graph

Treat each record as a node and add an edge between two records if their weighted similarity score >= threshold. Explain how to compute similarity efficiently.

3. Choose traversal algorithm

Use BFS or DFS to find all nodes reachable from the target. Discuss iterative vs recursive approaches and handling large graphs.

4. Optimize for scale

Address O(n²) pairwise comparison issue by using blocking, indexing, or approximate methods. Consider distributed processing if needed.

5. Analyze trade-offs and edge cases

Discuss time/space complexity, memory usage, and edge cases like disconnected graphs, self-loops, and threshold sensitivity.

Key Points to Mention

Graph representation (adjacency list vs matrix) and its impact on traversal efficiency
BFS/DFS implementation details, including visited set to avoid cycles
Weighted similarity computation and threshold application
Scalability concerns: O(n²) pairwise comparisons and mitigation strategies (e.g., blocking, LSH)
Handling dynamic updates if records are added/removed
Trade-offs between exact and approximate similarity for performance

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