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Goldman Sachs Asset & Wealth Management Data Engineer Interview Questions

The questions to prepare for a Goldman Sachs Asset & Wealth Management Data Engineer interview. Questions from real interview reports rank first. Updated daily.

46questions
~7htotal time
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1
CodingStart here. 7 questions · ~65 min
Flatten Nested Portfolio JSONMedium
Practice

Use recursive depth-first traversal to convert nested portfolio holdings into dot-delimited leaf paths.

function implementationjson parsingnested dictionariesGoldman Sachs Asset & Wealth Management
Top K Frequent Items Data StructureHard
Practice

Maintain changing item frequencies and return the top K items using a hash map and lazy min-heap.

Data Structurestop kHeapGoldman Sachs Asset & Wealth Management
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2
Pipelines21 questions · ~194 min
Pipeline Alerting and Monitoring DesignMedium

Set up pipeline monitoring and alerting that catches critical failures quickly while limiting noisy alerts.

InfrastructureToolsQualityGoldman Sachs Asset & Wealth Management
Consistency Across Data SourcesMedium

Approach for keeping records aligned and trustworthy when multiple source systems feed the same pipeline.

InfrastructureQualityGoldman Sachs Asset & Wealth Management
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3
SQL & Data Manipulation7 questions + 3 drills · ~95 min
SQL vs NoSQL Trade-offsEasy

Explain SQL vs NoSQL trade-offs, including schema design, consistency, scaling, and query flexibility.

JoinsData WranglingAggregationsGoldman Sachs Asset & Wealth Management
Optimize SQL for Millions of RowsHard

Assesses performance tuning skills for large-scale SQL workloads.

Performance Tuninglarge datasetsGoldman Sachs Asset & Wealth Management
Database-Focused Technical QuestionsMedium
Practice
Practice drill

Summarize database interview responses by topic, including unanswered topics and null scores.

databasesdatabase queryingAggregationsGoldman Sachs Asset & Wealth Management
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
Contact Center Agent Performance MetricsHard
Practice
Practice drill

Compute daily agent call KPIs and SLA using joins, aggregations, and window ranking in a contact center model.

ETLAggregationsData ModelingADP
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4
Behavioral & Leadership9 questions · ~83 min
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5
More topics2 questions · ~18 min
Low-Latency Market Data IngestionHard

Assesses your system design choices for fast, reliable ingestion of market data at scale.

low latencyreal-time ingestionGoldman Sachs Asset & Wealth Management
Logging Strategy for Distributed SystemsHard

Evaluates your approach to observability, traceability, and operational debugging in distributed pipelines.

production systemsdistributed systemsGoldman Sachs Asset & Wealth Management
The finish line: interview-readyComplete all 46 questions plus 3 hands-on drills to finish this plan.