Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Amazon data scientist interview that went deep into data engineering territory pretty fast. The whole thing centered on a single scenario about incremental loads and deduplication, which sounds straightforward until you're actually in it trying to explain idempotency on the spot.

Questions Asked (1)

Walk through how you'd design an incremental daily load process for a large relational table. What steps would you take, what problems came up, and how do you detect whether a specific row has already been loaded?

System DesignData ModelingTechnical Trade-offs

Author's notes

This one sprawled in ways I didn't expect.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: data volume, update frequency, latency tolerance, and source system. Then outline a high-level design covering extraction, staging, transformation, and loading with idempotency and incremental logic. Finally, discuss trade-offs, failure handling, and how you'd detect and handle duplicates or updates.

Pro tip: Emphasize idempotency and exactly-once semantics: use a natural key or hash of the row plus a load timestamp to detect changes, and design the process so re-running a failed load doesn't duplicate data. Mention partitioning and indexing strategies to keep the load performant as the table grows.

1. Clarify Requirements and Constraints

Ask about data volume, daily delta size, source system capabilities (e.g., CDC, timestamps), SLA for load completion, and whether updates/deletes need to be captured. This shapes the incremental strategy.

2. Design the Incremental Extraction

Use a high-water mark (e.g., last load timestamp or max ID) or change data capture (CDC) to extract only new or changed rows. Ensure the source provides a reliable monotonic column or log.

3. Stage and Transform Data

Load extracted data into a staging table, then apply transformations (cleansing, deduplication, business logic) before merging into the target. This isolates errors and allows validation.

4. Load with Idempotency and Upsert Logic

Use MERGE or INSERT ... ON CONFLICT to upsert rows based on a unique key (e.g., primary key + version). Track load metadata (batch ID, timestamp) to detect and skip already-loaded rows.

5. Monitor, Validate, and Handle Failures

Implement checks for row counts, duplicates, and data quality. On failure, rollback or restart from the last successful checkpoint. Log metrics for observability.

Key Points to Mention

Idempotency: ensure re-running the load doesn't duplicate data, using unique keys and merge/upsert operations.
Change detection: use timestamps, version numbers, or CDC to identify new/updated rows.
Partitioning and indexing: optimize the target table for incremental loads and queries.
Error handling and retries: design for partial failures, with checkpointing and rollback.
Data quality checks: validate row counts, nulls, and referential integrity before and after load.
Trade-offs: batch vs. streaming, latency vs. complexity, and cost implications.

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