Dataford
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Prep plan
Updated weekly · Last refresh Aug 30

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 weekly.

18questions
~2htotal time
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1
SQL & Data ManipulationStart here. 9 questions + 3 drills · ~102 min
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
Handling Deadlocks in Concurrent TransactionsMedium

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

deadlocksdatabase managementmysqlFull Visibility
Clustered vs Non-Clustered IndexesMedium

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

JoinsData WranglingFull 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
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
Analyzing Operations Bottlenecks with SQLHard
Practice
Practice drill

Use multi-level CTEs, joins, and LAG to identify Samsung regional inventory shortages and late deliveries.

JoinsperformanceAggregationsSamsung ElectronicsApeel Sciences
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2
Pipelines4 questions · ~32 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
Pipeline Error Handling and AlertingMedium

Tests your reliability engineering practices for production data pipelines.

error handlingalertingFull Visibility
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3
Behavioral & Leadership5 questions · ~40 min
Prioritizing Conflicting High-Stakes WorkEasy

Tests prioritization under pressure, stakeholder management, and ownership when multiple urgent requests compete for limited time.

Stakeholder ManagementOwnershipDealing With AmbiguityFull Visibility
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The finish line: interview-readyComplete all 18 questions plus 3 hands-on drills to finish this plan.