reevo·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Reevo had me work through a paginated API aggregation problem that looked straightforward on the surface but kept revealing more layers. The error handling and sorting edge cases were where they really wanted to see your thinking.

Questions Asked (1)

Implement a function that fetches all pages from a paginated HTTP API returning team objects with name and wins, merges the results, sorts by wins descending and name ascending, and returns the top N team names. You also need to handle transient network errors with retries and backoff, rate limiting, and malformed records.

API & IntegrationsAlgorithms & Data StructuresSystem Design

Author's notes

I started with the pagination loop and got that working pretty quickly, stopping when a page comes back empty.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a modular design that separates fetching, error handling, and processing. Walk through the implementation step-by-step, emphasizing robustness and efficiency, and conclude with testing and potential optimizations.

Pro tip: Mention that you would use exponential backoff with jitter for retries to avoid thundering herd problems, and that you would respect rate limit headers like Retry-After to dynamically adjust delays.

1. Clarify Requirements and Edge Cases

Ask about pagination details (page size, total pages, cursor vs offset), rate limit specifics, retry policy, and what constitutes a malformed record. Confirm the expected output format and handling of ties.

2. Design the Fetching Strategy

Plan to fetch pages sequentially or concurrently with a limit, using a retry mechanism with exponential backoff and jitter. Incorporate rate limiting by respecting headers or using a token bucket.

3. Implement Data Validation and Merging

Validate each team object, skipping or logging malformed records. Merge valid teams into a collection, handling duplicates by summing wins or taking the latest.

4. Sort and Select Top N

Sort the merged list by wins descending and name ascending, then extract the top N team names. Consider using a heap for large datasets to optimize.

5. Test and Optimize

Write unit tests for error scenarios, rate limiting, and malformed data. Discuss potential optimizations like caching, parallel fetching with limits, and memory efficiency.

Key Points to Mention

Exponential backoff with jitter for retries to handle transient errors and avoid overwhelming the server.
Respecting rate limit headers (e.g., Retry-After, X-RateLimit-Remaining) and implementing client-side throttling.
Robust error handling: distinguishing between retryable and non-retryable errors, and logging malformed records without failing the entire process.
Efficient merging and sorting: using a dictionary to aggregate wins, and a heap for top N selection when N is small relative to total teams.
Pagination handling: following next links or incrementing page numbers until no more data, with safeguards against infinite loops.
Testing strategy: mocking HTTP responses to simulate errors, rate limits, and malformed data, and verifying correct output.

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