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Great Day Improvements: A Family of Brands Data Engineer Interview Questions

The questions to prepare for a Great Day Improvements: A Family of Brands Data Engineer interview. Questions from real interview reports rank first. Updated weekly.

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~3htotal time
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1
PipelinesStart here. 15 questions · ~126 min
Data Cleaning in ETL PipelinesEasy

Approach for cleaning and preparing raw data inside an ETL pipeline.

Data WranglingETLQualityGreat Day Improvements: A Family of Brands
Choosing Batch vs Real TimeHard

Evaluate when a pipeline should use stream processing versus scheduled batch based on latency, cost, complexity, and data quality needs.

Stream ProcessingBatch ProcessingDependenciesGreat Day Improvements: A Family of Brands
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityGreat Day Improvements: A Family of Brands
Cloud Platform Experience for PipelinesMedium

Describe practical experience building pipelines on AWS, including orchestration, security, and data quality.

InfrastructureETLData ModelingGreat Day Improvements: A Family of Brands
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2
Coding4 questions · ~34 min
Time Complexity of Sorting AlgorithmsEasy

Compare common sorting algorithms by best, average, and worst-case time complexity and explain when each is appropriate.

MathArraysSortingGreat Day Improvements: A Family of Brands
Clean a Dataset FunctionEasy

Tests data cleaning logic, edge case handling, and practical coding skills.

Hash TablesArraysStringsGreat Day Improvements: A Family of Brands
Optimize a Data Retrieval AlgorithmMedium

Tests performance analysis and algorithmic thinking for faster data access.

Hash TablesSearchingGreedyGreat Day Improvements: A Family of Brands
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3
More topics1 question · ~8 min
Optimize a Slow-Running QueryMedium

Tests query tuning skills using execution plans, indexing, and query rewrites.

Joinsquery optimizationAggregationsGreat Day Improvements: A Family of Brands
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 20 questions plus 3 hands-on drills to finish this plan.