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Equinix Data Engineer Interview Questions

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

20questions
~3htotal time
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1
PipelinesStart here. 6 questions · ~48 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityEquinix
Handling Missing Data in PipelinesMedium

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

InfrastructureETLBatch ProcessingEquinix
Diagnose an Integration Pipeline IssueMedium

Structured approach to diagnose failures in an ETL integration, from source extraction through orchestration, data quality, and idempotent recovery.

ToolsDependenciesQualityEquinix
Architecting a BI Data WarehouseHard

Tests your ability to design warehouse architecture for reliable BI performance and usability.

ETLBatch ProcessingData ModelingEquinix
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2
Behavioral & Leadership11 questions · ~88 min
Leading an Ambiguous Data ProjectEasy

Tests leading through ambiguity by creating structure, prioritizing effectively, and driving cross-functional execution to a measurable result.

LeadershipOwnershipPrioritizationEquinix
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3
More topics3 questions · ~24 min
SQL Query Performance TuningHard

Tests ability to diagnose and improve SQL performance using practical techniques.

SubqueriesJoinsCTEsEquinix
Implement a Hash TableMedium

Tests your understanding of hashing, collisions, and implementing core data structure operations.

Hash TablesLinked ListsArraysEquinix
Structured vs Unstructured DataEasy

Tests your foundational understanding of data types used in analytics and modeling.

JoinsData WranglingEquinix

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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
Resolve Conflicting Customer AttributesMedium
Practice
Practice drill

Use joins, CTEs, and row ranking to resolve conflicting customer profile values across ACME House systems.

JoinsData WranglingQualityRamsey SolutionsACME House
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