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Updated weekly · Last refresh Oct 17

Purdue University Data Engineer Interview Questions

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

37questions
~5htotal time
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1
PipelinesStart here. 19 questions · ~155 min
Legacy Database to Observability PlatformMedium

Explain how to connect a legacy on premises database to a cloud observability platform, including API protocol and security choices.

data integrationAPIsobservabilityPurdue University
Star vs Snowflake SchemaEasy

Compare star and snowflake schemas in a warehouse pipeline, including structure and transformation trade-offs.

snowflake schemastar schemaData ModelingPurdue University
Describe a Complex Transformation PipelineMedium

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

ETLData ModelingQualityPurdue University
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2
SQL & Data Manipulation11 questions + 3 drills · ~120 min
Classify Orders with CASE WHENEasy

Explain how CASE WHEN adds conditional logic to SQL queries for labeling, transforming, and aggregating data.

Data WranglingCase WhenAggregationsPurdue University
SQL Query on Provided DataMedium

Assesses your ability to write correct SQL queries from limited context and provided data samples.

sqllinuxPurdue University
Window Functions for Yearly RankingMedium

Tests your ability to use window functions for ordered analytics within partitions.

Window FunctionsRankingRunning TotalsPurdue University
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
Python or R Analysis LibrariesMedium
Practice
Practice drill

Count unique respondents by Python or R library and return the most commonly used libraries first.

analytics toolsData ManipulationData WranglingOklahoma City Thunder
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3
Coding7 questions · ~57 min
Clean Missing Values and Aggregate by DepartmentEasy

Tests your ability to implement correct data cleaning and aggregation logic in Python.

Hash TablesMathArraysPurdue University
Optimize Slow Daily Batch ProcessingMedium

Tests your performance troubleshooting skills for batch data pipelines.

ArraysSortingGreedyPurdue University
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The finish line: interview-readyComplete all 37 questions plus 3 hands-on drills to finish this plan.