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Top 21
Prep plan
Updated weekly · Last refresh Oct 17

Resultant Data Engineer Interview Questions

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

21questions
~3htotal time
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1
PipelinesStart here. 6 questions · ~53 min
Batch vs Streaming Data ProcessingEasy

Compare batch and streaming data processing, including when each fits best in a pipeline.

Stream ProcessingETLBatch ProcessingResultant
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityResultant
ML Backend in a Web AppMedium

Assesses your ability to connect ML serving with data engineering considerations.

Machine LearningResultant
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2
Behavioral & Leadership11 questions · ~97 min
Shifting Requirements During a SprintMedium

Tests how you handle ambiguity and re-prioritize mid-execution while aligning stakeholders and maintaining delivery momentum.

AgileadaptabilityDealing With AmbiguityResultant
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3
More topics4 questions · ~35 min
Optimizing Slow SQL JoinsHard

Tests query tuning skills for large-scale SQL performance and join optimization.

Joinsperformancequery optimizationResultant
Aggregate Spend Per ClientEasy
Practice

Use a hash map to aggregate transaction amounts by client in a single pass.

Hash TablesaggregationCodingResultant
Production Python Error HandlingMedium

Tests reliability practices for production pipelines, including error handling and observability.

error handlingResultant
Generators for Memory EfficiencyMedium

Tests knowledge of Python execution models and memory-efficient data processing.

large datasetspythonResultant

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
SQL to Find Operational InefficiencyMedium
Practice
Practice drill

Identify Revolut merchants with high payment failure rates and report related support-resolution metrics.

Joinsbusiness problemsqlRevolut
Schema Design for User ActivityHard
Practice
Practice drill

Design normalized activity tables and query each user's recent activity count and latest event efficiently.

database optimizationschema designquery optimizationAmazon Web Services
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
The finish line: interview-readyComplete all 21 questions plus 3 hands-on drills to finish this plan.