Question 1

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 2

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....

Question 3

Describe a time when you had to deal with a major data quality issue. How did you handle it?

Accepted Answer

**STAR**: **Situation**: Revenue metrics doubled overnight. **Task**: Fix and restore trust. **Action**: Traced to duplicate join in pipeline. Deployed fix; ran backfill. Added unique constraint tests. Communicated to stakeholders with timeline. **Result**: Metrics restored; no recurrence....

Question 4

Describe the concept of data sharding and when to use it.

Accepted Answer

**Why Sharding Exists**: Single-node storage and throughput limits cap scalability. Sharding horizontally partitions data by a shard key (e.g., user_id, region) across N nodes, enabling linear scale-out for reads/writes.

**Architectural Logic**: Each shard holds a subset; routing uses `hash(key) mod N` or consistent hashing....

Question 5

Explain how you ensure data security and compliance in sensitive data projects.

Accepted Answer

**Situation**: Financial/healthcare data requires encryption, access control, auditing.

**Action**: (1) Encryption—at rest (AES-256, SSE-KMS), in transit (TLS 1.2+); (2) Access—IAM, RBAC, column/row-level security; (3) Masking—PII anonymization for dev/test; (4) Auditing—access logs, lineage; (5) Compliance—GDPR, HIPAA, SOC2.

**Goldman Context**: Data residency, retention policies....

Question 6

How do you prioritize competing demands in a high-pressure environment?

Accepted Answer

**Framework**: (1) Impact vs effort—high-impact, low-effort first; (2) Dependencies—unblock others; (3) SLAs—customer commitments; (4) Communicate—set expectations; renegotiate; (5) Delegate—don't bottleneck. Example: incident vs feature—incident first; ETA to stakeholders. Goldman: regulatory, client first....

Question 7

How would you model hierarchical data in a relational database?

Accepted Answer

Choose model based on read/write patterns and depth. WHY: Hierarchy queries (ancestors, descendants, paths) vary in cost across models. OPTIONS: (1) Adjacency list (parent_id)—simple, recursive CTEs; expensive for deep trees. (2) Path enumeration (/1/2/5/)—fast reads, costly updates. (3) Closure table (ancestor, descendant, depth)—excellent read-heavy; extra storage. (4) Nested sets—complex updates....

Goldman Sachs General Interview Questions

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Goldman Sachs General Interview Questions

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