Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

Jan 2025Remote

Summary

Amazon Data Scientist technical screen, one meaty SQL problem that took up basically the whole session. More complex than I expected for a phone round.

Questions Asked (1)

Given a table of employee punch-in and punch-out events, write SQL to compute daily hours worked per employee. Your solution needs to correctly pair each 'in' event with the next 'out', split overnight shifts across calendar days, cap unmatched 'in' events at 23:59:59, ignore orphan 'out' events, round totals to the nearest 0.25 hour, and flag any days with data quality issues like overlaps or consecutive same-action events.

Data ModelingSystem DesignTechnical Trade-offs

Author's notes

This one wrecked me a little.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model and edge cases, then outline a step-by-step SQL approach using window functions to pair events and handle overnight shifts. Emphasize data quality checks and rounding, and discuss trade-offs between simplicity and robustness.

Pro tip: Mention that you would first validate assumptions with the interviewer and propose a solution that balances correctness with performance, such as using a single pass with window functions rather than multiple self-joins.

1. Clarify Requirements and Edge Cases

Confirm the table schema, event types, and how to handle edge cases like overnight shifts, unmatched events, and data quality flags. Ask about expected data volume and performance constraints.

2. Pair In/Out Events with Window Functions

Use LEAD/LAG or ROW_NUMBER to pair each 'in' with the next 'out' per employee, ensuring correct ordering by timestamp. Handle consecutive same-action events by flagging them.

3. Split Overnight Shifts and Cap Unmatched Ins

For shifts spanning midnight, split into two rows: one ending at 23:59:59 and one starting at 00:00:00. For unmatched 'in' events, cap at 23:59:59 of the same day.

4. Compute Daily Hours and Round

Calculate duration per segment, sum by employee and date, then round to nearest 0.25 hour using ROUND(hours * 4) / 4.

5. Flag Data Quality Issues

Identify overlaps (where an 'in' occurs before the previous 'out') and consecutive same-action events, and add a flag column or separate output for these anomalies.

Key Points to Mention

Use of window functions (LEAD, LAG, ROW_NUMBER) to pair events efficiently.
Handling overnight shifts by splitting at midnight and attributing hours to correct calendar days.
Capping unmatched 'in' events at 23:59:59 and ignoring orphan 'out' events.
Rounding to nearest 0.25 hour using arithmetic (e.g., ROUND(hours * 4) / 4).
Data quality flags for overlaps and consecutive same-action events.
Performance considerations: avoid multiple self-joins, use partitioning by employee and ordering by timestamp.

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