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N26 Interview Questions

The questions to prepare for N26 interviews, across all roles. Questions from real interview reports rank first. Updated weekly.

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1
MetricsStart here. 3 questions · ~24 min
Investigate User Engagement DeclineMedium

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

RetentionDiagnosisEngagement MetricsNN26
Define Success Metrics for FeaturesMedium

Set a clear north star, supporting KPIs, leading indicators, and guardrails for a new product feature.

North Star MetricLagging IndicatorsKPIsNN26
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2
Execution6 questions · ~48 min
Prioritize Conflicting Engineering DeadlinesMedium

Explain how you would prioritize competing engineering deadlines when stakeholders, business impact, and delivery risk are all in tension.

Trade-offsRoadmappingRisk AssessmentNN26
Prioritize Across Multiple ProjectsEasy

Explain how you prioritize work across multiple operational projects with competing deadlines, impact, and stakeholder pressure.

RoadmappingScope ManagementPrioritizationNN26
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3
Behavioral & Leadership37 questions · ~296 min
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4
More topics4 questions · ~32 min
Common Pitfalls in Experiment ResultsHard

Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.

PeekingNovelty EffectSample Ratio MismatchNN26
Handling Missing and Dirty SQL DataMedium

Explain how to profile, clean, and standardize missing or dirty data before analysis.

Data WranglingCase WhenQualityNN26
Balance Conversion and RetentionHard

Framework for deciding when to favor short-term conversion gains versus long-term retention in a product decision.

Feature PrioritizationUser NeedsValue PropositionNN26
Define Savings Feature SuccessMedium

Define success for a feature meant to increase member savings behavior.

Feature PrioritizationUser NeedsValue PropositionNN26

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Hands-on SQL practiceWrite and run real queries in the editor. 3 drills · ~30 min
Normalize Messy CRM Contact DataHard
Practice
Practice drill

Clean inconsistent CRM contacts by joining source tables, standardizing values, and flagging bad records.

Data WranglingCase WhenQualityAlphaSense
Validate Monthly Revenue Data QualityHard
Practice
Practice drill

Use joins, a CTE, and CASE logic to flag messy monthly order data and produce cleaned revenue by month.

Data WranglingCase WhenQualityLiterati
Profile Incomplete Customer RecordsEasy
Practice
Practice drill

Classify Best Buy customer records as Complete, Incomplete, or Inconsistent using CASE and null/blank checks.

Data WranglingCase WhenQualityBest Buy
The finish line: interview-readyComplete all 50 questions plus 3 hands-on drills to finish this plan.