Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Stripe coding round for a software engineer role. The problem was a KYC data validation exercise where you read business records from stdin and apply a bunch of rules across fields and records. Felt more like a backend systems task than a typical leetcode grind, which I didn't fully expect.

Questions Asked (1)

Given a stream of business records from standard input (each with fields like business name, address, tax ID, owners, and attached documents), implement a validation pipeline that checks format rules, cross-field consistency, duplicate detection across records, and mandatory document presence. Output per-record pass/fail results with reasons.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

The stdin/stdout part tripped me up more than the logic itself.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a modular pipeline with distinct validation stages (format, consistency, duplicates, documents). Discuss trade-offs like streaming vs. batch, exact vs. fuzzy duplicate detection, and how to scale with large data volumes.

Pro tip: Emphasize idempotency and error handling: validation should be deterministic and produce actionable reasons, and the pipeline should gracefully handle malformed input without crashing. Also, mention that duplicate detection can be optimized using hashing or indexing to avoid O(n^2) comparisons.

1. Clarify Requirements and Constraints

Ask about input format, expected volume, latency requirements, and whether validation rules are static or configurable. Confirm output format and error reporting expectations.

2. Design Modular Validation Stages

Break down validation into independent stages: format checks, cross-field consistency, duplicate detection, and document presence. Each stage should be pluggable and produce structured errors.

3. Address Duplicate Detection Strategy

Discuss methods for duplicate detection: exact match via hashing (e.g., tax ID), fuzzy matching for names/addresses, and using a sliding window or external store for streaming. Consider trade-offs between accuracy and performance.

4. Handle Streaming and Scalability

Explain how to process records in a streaming fashion, maintaining state for duplicates (e.g., using a hash set or Bloom filter) and ensuring memory efficiency. Mention backpressure and parallelism if needed.

5. Output and Error Reporting

Define output format: per-record pass/fail with reasons. Ensure reasons are clear and actionable. Discuss logging, metrics, and how to handle partial failures.

Key Points to Mention

Separation of concerns: each validation type as a separate module for maintainability and testability.
Trade-offs between exact and fuzzy duplicate detection, and how to choose based on business needs.
Use of efficient data structures (hash maps, Bloom filters) for duplicate detection in streaming.
Handling malformed input gracefully and providing detailed error messages.
Scalability considerations: processing large streams with limited memory, potential for parallelization.
Configurability of validation rules to adapt to changing business requirements.

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