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Sii Netherlands Data Engineer Interview Questions

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

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1
PipelinesStart here. 6 questions · ~48 min
Batch vs Stream Processing Trade-offsMedium

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

InfrastructureStream ProcessingETLSii Netherlands
Data Quality and Schema EvolutionMedium

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

schema evolutionData ModelingQualitySii Netherlands
Design Real-Time IoT Sensor PipelineHard

Design a real-time IoT telemetry pipeline processing 180K sensor events/sec with <10s latency, replayability, data quality checks, and warehouse delivery.

InfrastructureStream ProcessingDependenciesSii Netherlands
Preferred ETL and Transformation ToolsEasy

Explain your preferred extraction and transformation stack, and the reasoning behind those tool choices.

ToolsETLELTSii Netherlands
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualitySii Netherlands
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2
Behavioral & Leadership8 questions · ~64 min
Motivation and Learning in Data EngineeringEasy

Tests intrinsic motivation for data engineering and whether the candidate turns continuous learning into practical impact.

CommunicationLeadershipOwnershipSii Netherlands
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3
More topics2 questions · ~16 min
Optimizing Large-Scale QueriesMedium

Tests your ability to improve SQL performance using indexing, query planning, and data modeling for large datasets.

Performance Tuninglarge datasetsquery optimizationSii Netherlands
SQL, Joins, and Git ToolsMedium

Tests your core SQL skills and your ability to manage code with version control.

Sii Netherlands

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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 16 questions plus 3 hands-on drills to finish this plan.