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Amazon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jan 2019

Summary

Amazon BI Engineer interview with a SQL window functions question focused on device-level engagement analytics. Pretty straightforward if you know your windowing syntax, but the rolling date range part trips people up.

Questions Asked (1)

Q1

Given a table with date, device type, and answer counts, write a SQL query that returns a rolling 7-day sum of answers for each device, ordered by date.

Product Analytics & MetricsData Modeling
Author's notes

The partition by device part came naturally but I fumbled the range clause for a second.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and expected output, then use a window function with a RANGE-based frame to compute the rolling 7-day sum per device. Ensure the query handles missing dates correctly and orders results by device and date.

Pro tip: Mention that you would validate the rolling sum by manually checking a few rows, and discuss how to handle edge cases like devices with no answers on certain days.

1. Clarify requirements and schema

Confirm the table name, columns (date, device_type, answer_count), and whether the rolling sum should be per device and include only dates present or all dates in a range.

2. Choose the right window function

Use SUM() OVER (PARTITION BY device_type ORDER BY date RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW) to get a rolling 7-day sum that includes the current day and the previous 6 days.

3. Handle missing dates and ordering

If the table may have gaps, consider generating a date series per device and left joining to fill missing days with zero counts. Order the final result by device_type and date.

4. Write and test the query

Compose the SQL, test with sample data, and verify the rolling sum logic, especially around the boundaries of the 7-day window.

Key Points to Mention

  • Use of window functions with PARTITION BY device_type and ORDER BY date
  • RANGE frame with INTERVAL '6 days' PRECEDING to include 7 days total
  • Handling of missing dates by generating a date series or using a calendar table
  • Ordering the final result by device_type and date
  • Consideration of performance implications for large datasets
  • Validation of results with manual checks or sample data

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