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DTE Energy Interview Questions

The questions to prepare for DTE Energy interviews, across all roles. Questions from real interview reports rank first. Updated weekly.

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1
SQL & Data ManipulationStart here. 6 questions + 2 drills · ~71 min
Compare Conversion Rates by ChannelEasy
Practice

Calculate channel-level conversion rates from a single events table using GROUP BY and conditional aggregation.

Group ByCase WhenAggregationsDTE Energy
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
Resolve Conflicting Customer AttributesMedium
Practice
Practice drill

Use joins, CTEs, and row ranking to resolve conflicting customer profile values across ACME House systems.

JoinsData WranglingQualityRamsey SolutionsACME House
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2
Metrics6 questions · ~51 min
Diagnose a Customer Satisfaction DropMedium

Investigate a sudden drop in customer satisfaction and separate leading signals from the final NPS readout.

NPSLeading IndicatorsDiagnosisDTE Energy
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3
Execution6 questions · ~51 min
Structure a STAR Execution StoryEasy

Explain how to deliver a clear STAR story that highlights your personal ownership and execution impact.

Trade-offsSuccess CriteriaScope ManagementDTE Energy
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4
Pipelines6 questions · ~51 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityDTE Energy
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5
Product Sense6 questions · ~51 min
Find Pain Points from FeedbackMedium

Use customer feedback to identify the biggest pain points in the user journey.

User ResearchUser NeedsPain PointsDTE Energy
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6
Strategy6 questions · ~51 min
Estimate Digital Product Market SizeHard

Estimate the market size for a new digital product opportunity using a structured TAM, SAM, SOM approach.

EstimationTAM/SAM/SOMMarket SizingDTE Energy
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7
More topics14 questions · ~120 min
Sample Size and Power PlanningMedium

Reason about sample size, power, and minimum detectable effect before launching an experiment.

Hypothesis TestingPower AnalysisSample SizeDTE Energy
Bias Variance and RegularizationMedium

Explain the bias-variance tradeoff mathematically and how L1 and L2 regularization change model complexity and weights.

Bias-Variance TradeoffRegularizationSupervised LearningDTE Energy
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The finish line: interview-readyComplete all 50 questions plus 2 hands-on drills to finish this plan.