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Updated weekly · Last refresh Aug 30

Mutual of Omaha Data Engineer Interview Questions

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

21questions
~3htotal time
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1
SQL & Data ManipulationStart here. 3 questions + 2 drills · ~46 min
Second Highest Without AggregatesHard
Practice

Find the second highest salary in each department without using aggregate functions.

SubqueriesRankingSelf-JoinsMutual of Omaha
Primary Key vs Foreign KeyEasy

Tests relational database fundamentals and how keys enforce data integrity.

database designdata integrityMutual of Omaha
Optimizing SQL Query PerformanceMedium

Tests query tuning skills including indexing, partitioning, and execution plan reasoning.

Window FunctionsJoinsAggregationsMutual of Omaha
Top Customers by Last-Quarter SpendMedium
Practice
Practice drill

Join customers and transactions, filter to the previous quarter, aggregate spend, and return the top three customers by total spend.

Date FunctionsJoinsAggregationsApptioQuantiphiDiscover
Running Total and Customer RankingMedium
Practice
Practice drill

Use aggregation and window functions to calculate cumulative paid claims and rank Mutual of Omaha policyholders within each region.

Window FunctionsRankingRunning TotalsMutual of OmahaCostar GroupGoodyear
2
Pipelines14 questions · ~121 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityMutual of Omaha
Handling Missing Data in PipelinesMedium

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

InfrastructureETLBatch ProcessingMutual of Omaha
Explain ETL in Data EngineeringEasy

Explain the ETL process, why it matters, and how it fits into a practical data pipeline.

ETLOrchestrationQualityMutual of Omaha
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3
Coding4 questions · ~35 min
Algorithm Optimization ExampleHard

Tests performance troubleshooting and algorithmic thinking under real constraints.

Dynamic ProgrammingSortingGreedyMutual of Omaha
Data Engineering Programming LanguagesEasy

Tests technical breadth and suitability of languages for building data pipelines and transformations.

ArraysStringsMutual of Omaha
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The finish line: interview-readyComplete all 21 questions plus 2 hands-on drills to finish this plan.