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Updated weekly · Last refresh Aug 30

AURORA Data Engineer Interview Questions

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

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1
PipelinesStart here. 12 questions · ~109 min
Design Scalable Pipeline InfrastructureHard

Design the core pipeline infrastructure for a new project, with attention to orchestration, data quality, idempotency, and future scale.

InfrastructureToolsQualityAURORA
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityAURORA
Design Global Vehicle Telemetry PipelineHard

Design a global real-time telemetry pipeline for 500,000 active vehicles with high availability, replayability, and strong data quality controls.

Stream Processinghigh availabilitytelemetryAURORA
Describe a Complex Transformation PipelineMedium

Explain a complex ETL transformation you built, including the main challenges and how you handled them.

ETLData ModelingQualityAURORA
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2
More topics4 questions · ~36 min
Merge Two Sorted ArraysEasy
Practice

Merge two sorted arrays into one sorted array using a two-pointer linear scan.

ArraysSortingTwo PointersAURORA
SQL vs NoSQL Trade-offsEasy

Explain SQL vs NoSQL trade-offs, including schema design, consistency, scaling, and query flexibility.

JoinsData WranglingAggregationsAURORA
SQL vs NoSQL for Go-to-Market DataEasy

Explain how SQL and NoSQL differ in schema, consistency, scaling, and Demandbase-style analytics use cases.

JoinsData WranglingAURORA
Deduplicating Records FunctionMedium

Tests algorithmic thinking and practical handling of duplicates for reliable datasets.

Hash TablesArraysSortingAURORA

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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.