Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Amazon SWE interview with a meaty data engineering problem. The whole thing revolved around one big question but they really dug into the edges: scale, correctness, testing. Left feeling like I handled the core logic fine but probably fumbled the streaming discussion.

Questions Asked (1)

Given a large user access log where each line has a timestamp, user_id, and URL, write a Python or SQL solution to identify returning customers, defined as users with visits on at least two distinct calendar days. Address timezone normalization, deduplication of same-day hits, memory constraints for large files, and time complexity. Include unit tests and example input/output.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I started with the obvious set-based approach: group by user_id, collect distinct dates, filter where count >= 2.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a scalable solution that handles timezone normalization, deduplication, and memory limits. Present both Python and SQL approaches, emphasizing trade-offs in time and space complexity, and include unit tests and example I/O to demonstrate correctness.

Pro tip: Mention that you would use a streaming approach with a set of (user_id, date) pairs, but if memory is a concern, you can use a Bloom filter or external sort. Also, highlight that timezone normalization should be done using a library like pytz or SQL's AT TIME ZONE, and that deduplication can be achieved by using a set or DISTINCT.

1. Clarify Requirements and Constraints

Ask about log format, timezone handling, file size, memory limits, and expected output format. Confirm that 'returning customer' means at least two distinct calendar days.

2. Design the Algorithm

Outline a two-pass or streaming approach: parse each line, normalize timestamp to a common timezone (e.g., UTC), extract date, and track unique (user_id, date) pairs. For large files, consider external sorting or a Bloom filter.

3. Implement in Python and SQL

Write Python code using a set to store (user_id, date) and count distinct dates per user. For SQL, use a query with GROUP BY user_id, COUNT(DISTINCT date) >= 2, after converting timestamps to dates with timezone conversion.

4. Analyze Complexity and Trade-offs

Discuss time complexity O(N) for streaming, space O(U*D) where U is users and D distinct days. For memory-constrained scenarios, propose partitioning or using a database with indexing.

5. Provide Tests and Example

Include unit tests covering edge cases: same-day multiple visits, timezone differences, empty input. Show example input lines and expected output list of returning users.

Key Points to Mention

Timezone normalization: convert all timestamps to a consistent timezone (e.g., UTC) before extracting dates.
Deduplication: use a set or DISTINCT to ensure multiple visits on the same day count as one.
Memory constraints: for large files, use streaming with a set if it fits, otherwise external sort or Bloom filter.
Time complexity: O(N) for single pass, but sorting may be O(N log N) if needed.
SQL approach: use GROUP BY user_id HAVING COUNT(DISTINCT date) >= 2 after timezone conversion.
Unit tests: include cases for multiple visits same day, different days, timezone edge cases, and empty input.

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