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Updated weekly · Last refresh Aug 30

EnergyHub Data Engineer Interview Questions

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

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1
PipelinesStart here. 5 questions · ~47 min
ETL vs ELT Trade-offsEasy

Compare ETL and ELT, and explain when ELT is the better pipeline pattern.

ETLELTData ModelingEnergyHub
Handling Missing Data in PipelinesMedium

Approach for handling missing data in an ML data pipeline, including validation, imputation, and safe downstream consumption.

InfrastructureETLBatch ProcessingEnergyHub
Data Integration Tools ExperienceEasy

Discuss the data integration tools you have used and how they fit into ETL, orchestration, and data quality workflows.

InfrastructureToolsETLEnergyHub
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2
Behavioral & Leadership4 questions · ~37 min
Working Through Team ConflictMedium

Assesses conflict resolution, communication, and ownership when collaborating with a difficult teammate under delivery pressure.

Conflict ResolutionteamworkcollaborationEnergyHub
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3
More topics4 questions · ~37 min
Merge Two Sorted Linked ListsEasy
Practice

Merge two sorted singly linked lists into one sorted list by relinking existing nodes.

RecursionLinked ListsSortingEnergyHub
SQL Aggregation Problem SolvingMedium

Tests your ability to write correct SQL for transforming and aggregating data.

Data WranglingGroup ByAggregationsEnergyHub
Queue Using Two StacksMedium

Tests your data structure design and reasoning about time and space complexity.

StackQueuedesignEnergyHub
Optimize a Slow SQL QueryMedium

Tests your performance troubleshooting skills for dv01-scale reporting queries.

JoinsData WranglingCTEsEnergyHub

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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 13 questions plus 3 hands-on drills to finish this plan.