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Global Lending Services Data Engineer Interview Questions

The questions to prepare for a Global Lending Services Data Engineer interview. Questions from real interview reports rank first. Updated weekly.

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1
PipelinesStart here. 9 questions · ~86 min
Handling Missing Data in PipelinesMedium

Approach for handling missing data in an ML data pipeline, including validation, imputation, and safe downstream consumption.

InfrastructureETLBatch ProcessingGlobal Lending Services
Explain ETL in Data EngineeringEasy

Explain the ETL process, why it matters, and how it fits into a practical data pipeline.

ETLOrchestrationQualityGlobal Lending Services
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityGlobal Lending Services
Structured vs Unstructured DataEasy

Explain how structured and unstructured data differ, and why that matters for pipeline design and downstream processing.

ETLData ModelingQualityGlobal Lending Services
Warehouse Analytics ExperienceEasy

Discuss practical experience using a data warehouse for analytics, including loading, transformation, orchestration, and data quality.

InfrastructureData ModelingGlobal Lending Services
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2
More topics3 questions · ~29 min
Merge Two Sorted ArraysEasy
Practice

Merge two sorted arrays into one sorted array using a two-pointer linear scan.

ArraysSortingTwo PointersGlobal Lending Services
Optimizing Slow SQL QueriesMedium

Tests your approach to diagnosing and optimizing SQL performance in production data systems.

JoinsData WranglingAggregationsGlobal Lending Services
Efficient User Session StorageMedium

Tests your ability to design data structures for fast session lookup and updates.

Hash TablesQueueArraysGlobal Lending Services

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
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
Handle Missing Financial Input ValuesMedium
Practice
Practice drill

Use joins, CTEs, and CASE logic to flag and fill missing financial fields for Qlik planning records.

Data WranglingETLQualityQlik
The finish line: interview-readyComplete all 12 questions plus 3 hands-on drills to finish this plan.