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Updated weekly · Last refresh Aug 30

Weedmaps Data Analyst Interview Questions

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

12questions
~2htotal time
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1
Product SenseStart here. 3 questions · ~24 min
Assess Feedback for Product ImprovementsEasy

Framework for evaluating customer feedback and turning it into prioritized product improvements.

User ResearchUser NeedsPain PointsWeedmaps
Data Analyst Quality at WeedmapsEasy

Tests product-mindedness and alignment with Weedmaps' data-driven culture.

User NeedsValue PropositionProduct VisionWeedmaps
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2
Metrics3 questions · ~24 min
Investigate User Engagement DeclineMedium

Investigate a 15% engagement decline by decomposing the metric, isolating root causes, and proposing actions.

RetentionDiagnosisEngagement MetricsWeedmaps
Identifying KPIs From DataMedium

Tests your KPI thinking and ability to translate data into measurable outcomes.

North Star MetricKPIsDiagnosisWeedmaps
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3
Pipelines3 questions · ~24 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityWeedmaps
Handling Missing Data in PipelinesMedium

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

InfrastructureETLBatch ProcessingWeedmaps
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4
More topics3 questions · ~24 min
Using SQL for AnalysisEasy

Tests SQL proficiency and practical data analysis workflows.

JoinsGroup ByAggregationsWeedmaps
Statistical Methods and Use CasesEasy

Tests knowledge of statistical methods and appropriate selection for real analysis tasks.

Confidence IntervalsRegressionHypothesis TestingWeedmaps
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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
Handle Missing Financial Input ValuesMedium
Practice
Practice drill

Use joins, CTEs, and CASE logic to flag and fill missing financial fields for Qlik planning records.

Data WranglingETLQualityQlik
The finish line: interview-readyComplete all 12 questions plus 3 hands-on drills to finish this plan.