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MetLife Data Engineer Interview Questions

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

18questions
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1
PipelinesStart here. 11 questions · ~88 min
Data Quality and Schema EvolutionMedium

Approach for handling schema changes and data quality checks in a high-volume data lake pipeline.

schema evolutionData ModelingQualityMetLife
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityMetLife
Privacy Compliance in Data PipelinesMedium

Approach for building privacy controls, lineage, and auditability into data pipelines that handle personal data.

Compliancedata privacyPipelinesMetLife
Design an End-to-End Data PipelineMedium

Approach for designing an end-to-end data pipeline from ingestion through transformation, storage, and downstream consumption.

data pipelinedesigningestionMetLife
Batch vs Stream Processing Trade-offsMedium

Compare batch and stream processing across latency, complexity, cost, and data quality in a modern analytics pipeline.

InfrastructureStream ProcessingETLMetLife
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2
Behavioral & Leadership5 questions · ~40 min
Handling a Critical Production IncidentMedium

Tests ownership and prioritization under pressure during a high-severity production incident, including communication and recovery discipline.

resilienceproduction issuesOwnershipMetLife
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3
More topics2 questions · ~16 min
Optimizing PySpark Data SkewHard

Tests advanced PySpark performance tuning for skewed data at scale.

sparkperformance analysisMetLife
Claims Reporting Data ModelingHard

Tests data modeling skills for high-performance insurance claims reporting.

financial dataperformanceData ModelingMetLife

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
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
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
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 18 questions plus 3 hands-on drills to finish this plan.