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Updated weekly · Last refresh Aug 30

Braze Data Engineer Interview Questions

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

23questions
~3htotal time
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1
SQL & Data ManipulationStart here. 6 questions + 3 drills · ~78 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 ModelingBraze
Optimize Slow SQL QueriesMedium

Tests query tuning skills, including indexing, execution plans, and rewriting strategies.

SubqueriesJoinsAggregationsBraze
Normalization vs Reporting DenormalizationMedium

Explain normalization for data integrity and when denormalization is useful for faster reporting and simpler analytical queries.

performancenormalizationdata integrityBraze
SQL Conversion Rate by CampaignEasy
Practice
Practice drill

Calculate daily campaign conversion rates with conditional aggregation, a CTE, and a campaign dimension join.

Date FunctionsGroup ByAggregationsNianticBrazeOutschool
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
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2
Pipelines11 questions · ~88 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityBraze
Handling Pipeline Errors at ScaleHard

Approach for stabilizing an automated workflow that is failing broadly, with focus on orchestration, data quality, idempotency, and rollback.

IdempotencyBackfillingQualityBraze
Python for ETL Pipeline TasksEasy

Discuss Python scripting experience for ETL, orchestration, and data quality tasks in data pipelines.

InfrastructureToolsETLBraze
Cloud AI Deployment Pipeline ExperienceMedium

Discuss experience building cloud-based AI pipelines, including orchestration, processing patterns, infrastructure choices, and data quality controls.

InfrastructureToolsQualityBraze
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3
Behavioral & Leadership6 questions · ~48 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 AmbiguityBraze
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The finish line: interview-readyComplete all 23 questions plus 3 hands-on drills to finish this plan.