Amazon·Data Scientist·Technical Phone Screen·Senior

SeniorPrefer not to say

Sep 2025Remote

Summary

Amazon data scientist technical screen, basically one massive SQL/Python design question that took the whole session. The problem was dense enough that I kept second-guessing whether they wanted a real production answer or just a sketch.

Questions Asked (1)

You need to load the last 7 days of orders from a noisy staging feed into a large fact table. Design a full approach that handles idempotent reruns, late-arriving updates, and duplicate detection. Include DDL, a dedupe CTE using window functions or aggregation, a MERGE statement that only updates when the incoming row is newer, a watermark strategy for catching late data, row hashing for prior-load detection, and at least two data-quality assertions that can trigger a rollback.

System DesignData ModelingTechnical Trade-offs

Author's notes

This was basically the whole interview compressed into one question.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a batch ETL with idempotency and late-data handling, then walk through a layered solution: staging with deduplication, a MERGE with update guards, and a watermark plus DQ checks. Emphasize trade-offs between correctness and performance, and show how each component addresses a specific failure mode.

Pro tip: In Amazon interviews, explicitly connect your design to operational realities like rerun frequency, data volume, and SLA impact—this shows you think beyond code and understand production data pipelines.

1. Clarify requirements and constraints

Ask about data volume, update frequency, acceptable latency, and whether the fact table is append-only or mutable. Confirm the definition of 'last 7 days' and how late data is expected.

2. Design staging and deduplication

Load raw data into a staging table, then use a dedupe CTE with ROW_NUMBER() partitioned by order_id ordered by update_timestamp DESC to keep the latest record per order.

3. Implement idempotent MERGE with update guard

Write a MERGE that inserts new orders and updates existing ones only when the incoming update_timestamp is greater than the target's, ensuring reruns don't regress data.

4. Add watermark and row hashing for late data

Maintain a watermark table storing the max update_timestamp processed; on each run, reprocess a lookback window (e.g., 3 days) to catch late updates. Use row hashing to detect unchanged rows and skip unnecessary updates.

5. Define data-quality assertions and rollback

Implement checks like row count within expected range and no duplicate order_ids in the target; if any fail, rollback the transaction and alert.

Key Points to Mention

Idempotency via MERGE and deduplication ensures reruns produce the same result.
Late-arriving updates handled by watermark lookback and update_timestamp guard.
Duplicate detection using ROW_NUMBER() or aggregation in a CTE.
Row hashing (e.g., MD5 of concatenated columns) to avoid unnecessary updates.
Data-quality assertions: row count thresholds, uniqueness checks, and null checks.
Trade-offs: lookback window size vs. cost, MERGE performance on large tables.

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