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Updated weekly · Last refresh Sep 22

E15 Group Data Engineer Interview Questions

The questions to prepare for a E15 Group Data Engineer interview. Questions from real interview reports rank first. Updated daily.

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1
PipelinesStart here. 5 questions · ~40 min
Design an End-to-End Data PipelineMedium

Approach for designing an end-to-end data pipeline from ingestion through transformation, storage, and downstream consumption.

data pipelinedesigningestionE15 Group
Handling Missing Data in PipelinesMedium

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

InfrastructureETLBatch ProcessingE15 Group
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityE15 Group
Learning New Technology QuicklyMedium

Tests your learning strategy, adaptability, and speed-to-productivity for new tools.

InfrastructureToolsETLE15 Group
Resolving a Significant Data IssueMedium

Tests your incident response, root-cause thinking, and data quality remediation.

ETLData ModelingQualityE15 Group
2
More topics3 questions · ~24 min
Optimizing a Slow QueryHard

Tests your ability to diagnose bottlenecks and improve runtime and resource usage.

JoinsData WranglingCTEsE15 Group
Preferred Work CultureMedium

Assesses your work preferences and how well they align with E15 Group’s delivery culture.

team dynamicsE15 Group
Act on Constructive FeedbackMedium

Tests coachability, growth mindset, and how feedback translates into improved sales execution.

coachabilityfeedback incorporationE15 Group

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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
Cleaning a Messy DatasetMedium
Practice
Practice drill

Filter invalid game events and normalize event names using PostgreSQL string and null handling.

missing valuesdata cleaningnull handlingQuantiumScopelyCarvana
The finish line: interview-readyComplete all 8 questions plus 3 hands-on drills to finish this plan.