Stripe·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

Jun 2026Remote

Summary

Stripe phone screen that was basically one big design problem dressed up as a coding question. The email event stream thing sounds simple but the follow-ups kept stacking and by the end I was juggling four different concerns at once.

Questions Asked (1)

You're given a stream of email events (send, open, click, bounce, unsubscribe), each with a type, recipient, campaign ID, and timestamp. Build a system that ingests these events and supports count queries by event type and campaign. Then extend it to handle time-window queries, top-recipient lookups, and deduplication of identical events.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Started fine with a hash map keyed on (type, campaign_id) and that covered the basic count.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., event volume, query patterns, latency needs), then design a simple ingestion pipeline with a storage layer that supports efficient aggregation. Iteratively extend the design to handle time-window queries, top-recipient lookups, and deduplication, discussing trade-offs at each step.

Pro tip: Emphasize deduplication using a unique event ID or a hash of (type, recipient, campaign, timestamp) and discuss idempotent ingestion to avoid double-counting, which is critical for reliable analytics.

1. Clarify Requirements and Scale

Ask about expected event volume, query frequency, latency requirements, and whether data can be approximate. This determines the choice of storage and indexing.

2. Design Ingestion and Storage

Propose a scalable ingestion pipeline (e.g., message queue) and a storage schema that supports fast counts by event type and campaign, such as a columnar store or a key-value store with pre-aggregated counters.

3. Implement Count Queries

Explain how to serve count queries efficiently, e.g., using materialized views or maintaining counters in a database, and discuss consistency vs. performance trade-offs.

4. Extend for Time-Window Queries

Describe how to handle time-window queries, such as using time-bucketed aggregates (e.g., per-minute counts) or a time-series database, and how to query across buckets.

5. Add Top-Recipient Lookups and Deduplication

For top recipients, maintain a leaderboard per campaign using a heap or sorted set. For deduplication, use a unique event ID or a hash and a dedup store (e.g., Bloom filter or key-value store) to filter duplicates before ingestion.

Key Points to Mention

Use of unique event IDs or hashing for deduplication and idempotent processing
Choice of storage: columnar (e.g., Parquet) for analytics vs. row-based for fast writes
Pre-aggregation and materialized views to speed up count queries
Time-bucketing (e.g., per-minute or per-hour) for efficient time-window queries
Data structures like heaps or sorted sets for top-K recipient lookups
Trade-offs between consistency, latency, and cost in a streaming system

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