Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Stripe coding screen, looked like a straightforward data problem but the efficiency angle is where they actually care. One question, clean setup, but you have to think past the brute force immediately.

Questions Asked (1)

Given a list of companies (each with an id, phone number, email, and name), and a target company id, return all companies that share at least one attribute (phone, email, or name) with the target company. How would you implement this efficiently?

Algorithms & Data StructuresSystem Design

Author's notes

My first instinct was to just loop through everything and compare, which works but they clearly wanted more.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., data size, update frequency, exact vs. fuzzy matching). Then propose an efficient solution using hash maps to index companies by each attribute, enabling O(1) lookups to find matches. Discuss trade-offs between time and space, and consider edge cases like duplicates and normalization.

Pro tip: Mention that in real-world systems, attributes like phone and email often need normalization (e.g., removing spaces, lowercasing) to ensure accurate matching, and that this can be done during indexing.

1. Clarify Requirements

Ask about data size, whether attributes are unique, if matching is exact or fuzzy, and if the list is static or dynamic. This determines the optimal approach.

2. Design Indexing Strategy

Create hash maps for each attribute (phone, email, name) mapping attribute values to lists of company IDs. This allows O(1) lookup per attribute.

3. Retrieve and Deduplicate Matches

For the target company, look up its phone, email, and name in the respective maps, collect all matching company IDs, and deduplicate using a set.

4. Analyze Complexity and Trade-offs

Explain that preprocessing takes O(N) time and space, and querying takes O(k) where k is the number of matches. Discuss alternatives like sorting or inverted indices for different scenarios.

5. Handle Edge Cases

Address normalization (e.g., phone formatting, email case), missing attributes, and potential collisions (e.g., common names). Suggest strategies like exact match on phone/email but fuzzy on name if needed.

Key Points to Mention

Hash map indexing for O(1) lookups per attribute
Deduplication using a set to avoid returning the same company multiple times
Normalization of attributes (e.g., lowercasing emails, stripping phone formatting)
Time and space complexity analysis (O(N) preprocessing, O(k) query)
Trade-offs between memory usage and query speed
Handling of missing or null attributes gracefully

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