Question 1

Describe a scenario where partitioning and bucketing would improve query performance.

Accepted Answer

Situation: An events table with billions of rows serving time-range and user-level analytics. Task: Achieve sub-second query latency while controlling storage and compute costs. Why Partitioning: Partition pruning at read time eliminates entire data scans—a query filtering by date_range only touches relevant partition dirs. This reduces I/O by orders of magnitude (e.g., 365 partitions → scan 1 vs all)....

Question 2

When would you choose a Snowflake schema over a Star schema?

Accepted Answer

Star: One fact, denormalized dimensions—simple, fewer joins, fast. Snowflake: Normalized dimensions (e.g., dim_product → dim_category → dim_category_group)—more joins, less redundancy. Why Snowflake: When dimension tables are large and shared—a single dim_category serves many products; denormalizing would duplicate millions of rows. Avoids inconsistent attributes across copies (e.g., category name updated in one place). Storage: Snowflake saves space when hierarchy is deep and wide....

Question 3

Implement a query to find the top 5 customers by total sales amount.

Accepted Answer

**Architectural Logic**: Two approaches. 1. GROUP BY + ORDER BY + LIMIT: SELECT customer_id, SUM(sales_amount) total_sales FROM sales GROUP BY customer_id ORDER BY total_sales DESC LIMIT 5. 2. Window: SELECT * FROM (SELECT customer_id, SUM(sales_amount) total_sales, RANK() OVER (ORDER BY SUM(sales_amount) DESC) rk FROM sales GROUP BY customer_id) t WHERE rk <= 5. **Why**: LIMIT is simpler, stops early in some engines....

Question 4

Write an SQL query to find duplicate emails in a users table.

Accepted Answer

**Architectural Logic**: Simple: SELECT email FROM users GROUP BY email HAVING COUNT(*) > 1. With context: SELECT email, COUNT(*) cnt, ARRAY_AGG(user_id) ids FROM users GROUP BY email HAVING COUNT(*) > 1. Window: SELECT DISTINCT email FROM (SELECT email, COUNT(*) OVER (PARTITION BY email) cnt FROM users) t WHERE cnt > 1. **Why**: GROUP BY + HAVING is standard; window useful if you need other columns per duplicate....

Question 5

Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?

Accepted Answer

Kafka ingestion with Spark Structured Streaming follows a standard pattern: readStream → parse → writeStream with checkpoint. **Architectural decisions**: (1) **startingOffsets**: 'earliest' for backfill, 'latest' for tail-only; use JSON per-partition offsets for exactly-once replay. (2) **Checkpoint**: Mandatory for exactly-once; stores offsets + write metadata; without it, duplicates or data loss on restart....

Question 6

Tell me about a time you handled a data pipeline failure during a critical operation.

Accepted Answer

Situation: Partition overwrite corrupted Delta table during critical op. Task: Restore and prevent. Action: Paused downstream; diagnosed via Delta log. Used time travel: RESTORE TABLE ... TO TIMESTAMP. Validated; re-ran from checkpoint. Implemented write-audit-commit, automated backups, staging validation....

Question 7

Compare batch processing and stream processing for financial data.

Accepted Answer

**Batch**: Reporting, reconciliation, EOD. Higher latency; simpler; better for large scans. **Stream**: Fraud, trading, alerts. Low latency; complex; exactly-once critical. **Trade-off**: Batch cheaper; stream real-time. **Best practice**: Use both—batch for EOD; stream for time-sensitive....

Question 8

Compute the moving average of daily transactions over a 7-day window.

Accepted Answer

**SQL**: SELECT trans_date, SUM(amount) AS daily, AVG(SUM(amount)) OVER (ORDER BY trans_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS ma_7d FROM transactions GROUP BY trans_date. **Logic**: Inner SUM for daily total; outer AVG over 7 rows. **Spark**: Group by date, then window....

Goldman Sachs Data Engineer Interview Questions

+16 More Questions with Expert Answers

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Goldman Sachs Data Engineer Interview Questions

+16 More Questions with Expert Answers

Other Companies