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Hennepin County, Minnesota Data Analyst Interview Questions

The questions to prepare for a Hennepin County, Minnesota Data Analyst interview. Questions from real interview reports rank first. Updated weekly.

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1
PipelinesStart here. 3 questions · ~25 min
Handling Missing Data in PipelinesMedium

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

InfrastructureETLBatch ProcessingHennepin County, Minnesota
Optimize Slow Spark ETL PipelineHard

Redesign a slow Databricks Spark ETL pipeline to cut runtime from 3 hours to under 60 minutes without breaking data quality or SLAs.

Hennepin County, Minnesota
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityHennepin County, Minnesota
2
Behavioral & Leadership9 questions · ~74 min
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3
More topics5 questions · ~41 min
Statistical Project WalkthroughMedium

Walk through a past project using hypothesis testing and regression to turn data into a decision.

RegressionHypothesis TestingStatistical SignificanceHennepin County, Minnesota
SQL Query for Data ExtractionEasy

Tests your SQL proficiency for retrieving and shaping data for analysis.

JoinsGroup ByAggregationsHennepin County, Minnesota
Improving Public Health DataMedium

Tests ability to translate public health goals into an analysis plan and actionable insights.

User NeedsUse CasesProduct VisionHennepin County, Minnesota
Metrics for Community Service SuccessMedium

Tests metric selection aligned to outcomes, equity considerations, and measurable impact.

KPIsLeading IndicatorsDiagnosisHennepin County, Minnesota
Cost-Benefit Analysis for ProgramsHard

Tests quantitative reasoning for evaluating program tradeoffs and decision-making.

Hypothesis TestingCausal InferenceExpected ValueHennepin County, Minnesota

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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
Top Customers by Net SalesMedium
Practice
Practice drill

Compute top 10 customers by net sales using joins and aggregations, ordering by revenue with deterministic tie-breaking.

SubqueriesJoinsData WranglingGain DigitalTCSZest AI
The finish line: interview-readyComplete all 17 questions plus 3 hands-on drills to finish this plan.