Affirm·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Affirm software engineering round, one meaty coding problem that was clearly designed to take the full hour if you let it. The problem looked like a data pipeline exercise but had enough edge cases baked in that you could easily sink 20 minutes on just the parsing.

Questions Asked (1)

Design and implement a system that reads offer redemption events from a CSV on stdin, tracks how many times each user has redeemed each offer within its valid time window, and outputs which offers each user can still redeem as of a given processing date.

System DesignAlgorithms & Data StructuresData Modeling

Author's notes

This one has more surface area than it first looks.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then outline a streaming solution that processes events in order, maintains per-user per-offer redemption counts within valid time windows, and outputs redeemable offers as of the processing date. Emphasize data modeling, time window handling, and scalability considerations.

Pro tip: Discuss how you would handle late-arriving events and out-of-order timestamps, as this demonstrates awareness of real-world data challenges and robustness.

1. Clarify Requirements and Assumptions

Ask about input format, event ordering, time window definition, and output format. Confirm whether processing date is provided or derived, and if events can be out of order.

2. Design Data Model and Storage

Choose data structures to track redemption counts per user per offer, considering memory vs. disk trade-offs. Consider using a hash map of user IDs to offer IDs to counts, and store offer validity windows separately.

3. Process Events and Enforce Time Windows

Stream events from stdin, parse each row, and update counts only if the event timestamp falls within the offer's valid window. Handle late events by either buffering or using a windowing mechanism.

4. Compute Redeemable Offers

For each user, determine which offers are still redeemable as of the processing date by checking remaining redemption limits and offer validity. Output results in the required format.

5. Discuss Scalability and Edge Cases

Address how the solution scales with large data, potential memory issues, and edge cases like multiple redemptions, expired offers, and users with no redemptions.

Key Points to Mention

Time window handling: inclusive/exclusive boundaries, time zones, and date formats.
Data structures: hash maps for O(1) lookups, and possibly a priority queue for expiring offers.
Streaming vs. batch processing: trade-offs and suitability for stdin input.
Handling late-arriving events: buffering, watermarking, or reprocessing.
Output format: grouping by user, listing offers with remaining counts.
Scalability: partitioning by user ID, using external storage if data exceeds memory.

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