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MSI Data Engineer Interview Questions

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

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1
PipelinesStart here. 3 questions · ~24 min
ETL vs ELT Trade-offsEasy

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

ETLELTData ModelingMMSI
Batch vs Stream Processing Trade-offsMedium

Compare batch and stream processing across latency, complexity, cost, and data quality in a modern analytics pipeline.

InfrastructureStream ProcessingETLMMSI
Data Quality and Schema EvolutionMedium

Approach for handling schema changes and data quality checks in a high-volume data lake pipeline.

schema evolutionData ModelingQualityMMSI
2
Behavioral & Leadership4 questions · ~32 min
Mentoring a Struggling TeammateEasy

Tests mentoring ability through a concrete example, including coaching approach, feedback style, and measurable impact on the other person's growth.

MentorshipCommunicationLeadershipMMSI
Complex Analysis Under Tight DeadlineMedium

Tests prioritization under pressure, analytical rigor, and ownership of data accuracy when deadlines are tight.

Data AnalysisAccuracydeadline managementMMSI
Handling Product Data Requirement DisagreementsMedium

Assesses how you align on data requirements and manage stakeholder conflict.

MMSI
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3
More topics2 questions · ~16 min
Star vs Snowflake for Sales AnalyticsMedium

Compare star and snowflake schemas for warehouse design, including trade-offs in normalization, query simplicity, and analytics performance.

JoinsData WranglingGroup ByMMSI
SQL Query Optimization With JoinsHard

Tests advanced SQL tuning skills and your ability to improve complex query performance.

Performance TuningJoinsMMSI

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