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Updated weekly · Last refresh Aug 30

Wish Data Analyst Interview Questions

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

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1
SQL & Data ManipulationStart here. 7 questions + 3 drills · ~88 min
Handling Missing Values in SQLEasy

Explain how to detect and handle NULL values in SQL using filtering, COALESCE, CASE, and business-aware imputation.

Data WranglingETLCase WhenWish
Handling Missing DataHard

Tests data quality handling and correct treatment of missingness.

Window FunctionsData WranglingCTEsWish
Normalization in Database DesignEasy

Tests your fundamentals of relational modeling and data integrity.

JoinsData WranglingData ModelingWish
Top Customers by Region VolumeMedium
Practice
Practice drill

Find the top 3 customers in each region by transaction volume using joins, aggregation, and window ranking.

Window FunctionsJoinsRankingJPMorganChaseSilicon Valley BankNavi
Daily Active Users and ChurnMedium
Practice
Practice drill

Calculate Wish daily active users and next-day churn with deduplication, CTEs, joins, and date aggregation.

sql queriesWish
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
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2
Metrics3 questions · ~25 min
Analyzing a 20% Sales DropMedium

Tests structured root-cause analysis using metrics, segmentation, and data validation.

Lagging IndicatorsLeading IndicatorsDiagnosisWish
Finding Buying Behavior TrendsMedium

Tests your ability to design analyses that uncover behavioral trends and actionable insights.

Funnel AnalysisDiagnosisEngagement MetricsWish
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3
More topics3 questions · ~25 min
Data Driven Business RecommendationEasy

Explain how you used data analysis to make a business recommendation and drive a clear product decision.

User ResearchUser NeedsUse CasesWish
A/B Testing for Product FeaturesMedium

Explain how to design and evaluate an A/B test for a product feature, including metrics, MDE, sample size, and guardrails.

Hypothesis TestingStatistical SignificanceA/B TestingWish
Collaboration Across DisciplinesEasy

Tests communication and stakeholder management across product, engineering, and analytics.

User Needscross-functional collaborationWish

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