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SUNY Buffalo Data Analyst Interview Questions

The questions to prepare for a SUNY Buffalo Data Analyst interview. Questions from real interview reports rank first. Updated weekly.

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1
Behavioral & LeadershipStart here. 6 questions · ~48 min
Prioritizing Across Competing Client ProjectsEasy

Tests prioritization under pressure across multiple projects, including trade-off judgment, stakeholder communication, and ownership of outcomes.

OwnershipPrioritizationSUNY Buffalo
Leading Through an Ambiguous Project CrisisMedium

Tests ownership under ambiguity: how you prioritize, align stakeholders, and recover a project when the path forward is unclear.

CommunicationOwnershipDealing With AmbiguitySUNY Buffalo
Using Data Under AmbiguityMedium

Tests data-driven problem solving in ambiguous situations, with emphasis on ownership, stakeholder alignment, and measurable business impact.

CommunicationProblem SolvingOwnershipSUNY Buffalo
Presenting Unwelcome Analytical FindingsMedium

Tests whether you can influence resistant non-technical stakeholders with clear, data-driven communication while preserving trust and ownership.

Influence Without AuthorityStakeholder ManagementCommunicationSUNY Buffalo
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2
More topics4 questions · ~32 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualitySUNY Buffalo
Investigating Enrollment DropsMedium

Tests your ability to diagnose enrollment changes using metrics, segmentation, and data-driven investigation.

Lagging IndicatorsLeading IndicatorsDiagnosisSUNY Buffalo
SQL and Data Tooling ProficiencyEasy

Tests practical SQL and analytics tooling experience and project impact.

ToolsJoinsData WranglingSUNY Buffalo
Handling Missing Data in PipelinesMedium

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

InfrastructureETLBatch ProcessingSUNY Buffalo

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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
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
The finish line: interview-readyComplete all 10 questions plus 3 hands-on drills to finish this plan.