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Prep plan
Updated weekly · Last refresh Oct 17

Full Visibility Data Engineer Interview Questions

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

18questions
~3htotal time
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1
SQL & Data ManipulationStart here. 9 questions + 2 drills · ~98 min
Handling Deadlocks in Concurrent TransactionsMedium

Explain how to detect, prevent, and recover from deadlocks in concurrent PostgreSQL transaction workloads.

deadlocksdatabase managementmysqlFull Visibility
Star vs Snowflake for Meta AnalyticsEasy

Explain star and snowflake schemas, their tradeoffs, and when to use each in Meta-scale analytics systems.

Full Visibility
OLTP vs OLAP Database DesignMedium

Explain OLTP vs OLAP designs, including schema shape, workload patterns, and when each is appropriate in a data platform.

financial dataperformanceData ModelingFull Visibility
Clustered vs Non-Clustered IndexesMedium

Explain how clustered and non-clustered indexes differ in storage, lookup behavior, and query performance.

JoinsData WranglingFull Visibility
Claims Reporting Data ModelingHard
Practice
Practice drill

Build a claims reporting query that balances normalized writes with monthly reporting performance.

database optimizationfinancial dataperformanceMetLife
SQL to Find Operational InefficiencyMedium
Practice
Practice drill

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

Joinsbusiness problemsqlRevolut
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2
Pipelines4 questions · ~35 min
Complex ETL Pipeline ArchitectureHard

Explain the architecture of a complex ETL pipeline built from scratch, including orchestration, data quality, idempotency, and backfill strategy.

InfrastructureETLData ModelingFull Visibility
Data Quality and Schema EvolutionMedium

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

schema evolutionData ModelingQualityFull Visibility
Batch vs Stream and Tool ChoiceMedium

Tests your understanding of processing paradigms and selecting the right tooling for data delivery needs.

Stream ProcessingToolsBatch ProcessingFull Visibility
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3
Behavioral & Leadership5 questions · ~43 min
Owning a Pipeline MistakeMedium

Tests ownership after a pipeline mistake, including remediation, communication, and learning-driven process improvement.

learning agilityproduction issuesOwnershipFull Visibility
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The finish line: interview-readyComplete all 18 questions plus 2 hands-on drills to finish this plan.