Pro90 ProblemsSQL + Python
Payments SQL & Python Interview Questions
Payment processors handle transaction authorization, fraud scoring, and settlement at massive scale. These SQL and Python challenges are modeled after analytical work at Stripe, PayPal, Square, Adyen, Visa, Mastercard, Klarna, Affirm, Plaid, Marqeta, and more. Practice transaction success rate analysis, merchant GMV tracking, chargeback pattern detection, payment failure mode analysis, and authorization rate optimization.
Companies working in Payments
These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.
Company names and logos are trademarks of their respective owners, used here only to describe the kind of data these companies work with. Let's Data Science is not affiliated with, endorsed by, or sponsored by any company shown. Practice problems are original works and are not real interview questions from these companies. Rights & takedowns.
Difficulty Distribution
Easy
15
17% of problems
Medium
32
36% of problems
Hard
39
43% of problems
Expert
4
4% of problems
What You'll Practice
Transaction success analysis
Fraud scoring metrics
Merchant GMV reporting
Chargeback analysis
Payment method performance
Settlement analytics
Dispute resolution metrics
Authorization rate optimization
Topics Covered
SQL· 9
aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions
Python· 12
eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window
All Problems90 total
01
Active E-Commerce MerchantsPro
SQLEasy02High-Value Captured ChargesPro
SQLEasy03Failed Charges With Failure CodePro
SQLEasy04Active Credit Card Payment MethodsPro
SQLEasy05US Merchants With Daily Payout SchedulePro
SQLMedium06Charges With Merchant DetailsPro
SQLEasy07Charges With Customer and Payment MethodPro
SQLMedium08Refunds With Charge and Merchant ContextPro
SQLMedium09Disputes With Charge and Customer InfoPro
SQLMedium10Merchants Without RefundsPro
SQLMedium11Charges With Full Payment ContextPro
SQLHard12Charge Volume by StatusPro
SQLEasy13Total Captured Revenue per MerchantPro
SQLMedium14Average Charge Amount by CurrencyPro
SQLMedium15Platform Fee Revenue by Transaction TypePro
SQLMedium16Top 5 Merchants by Transaction VolumePro
SQLHard17Merchants With High Charge Failure RatePro
SQLHard18Rank Merchants by Charge VolumePro
SQLMedium19Customer Charge Sequence NumberPro
SQLMedium20Daily Platform Fee Running TotalPro
SQLHard21Largest Charge per MerchantPro
SQLHard22Merchant Charge 3-Transaction Moving AveragePro
SQLHard23Charge Amount Change From PreviousPro
SQLHard24Charge Amount Quartile AnalysisPro
SQLHard25Merchants Above Average Charge AmountPro
SQLMedium26Customers With Card and Wallet MethodsPro
SQLHard27Latest Charge per MerchantPro
SQLHard28MCC Category Revenue With RankPro
SQLHard29Payment Method Types With Above-Average Charge ValuePro
SQLHard30November 2024 ChargesPro
SQLEasy31Monthly Charge Volume and RevenuePro
SQLMedium32Average Authorization-to-Capture Time by MerchantPro
SQLMedium33Dispute Resolution Time AnalysisPro
SQLHard34Charges With Amount TierPro
SQLEasy35Payment Methods With Detailed Type LabelPro
SQLMedium36Merchant Charge Activity SummaryPro
SQLHard37Countries With Merchants or CustomersPro
SQLMedium38Merchants With Charges But No RefundsPro
SQLMedium39Merchant Health ScorecardPro
SQLHard40Payment Method Performance ReportPro
SQLHard41Customer Spending SummaryPro
SQLHard42Refund Reason AnalysisPro
SQLHard433D Secure Impact on Charge OutcomesPro
SQLHard44Payout Performance DashboardPro
SQLHard45Currency Performance ComparisonPro
SQLHard46Active Merchant ProfilesPro
PYTHONEasy47Charge Status CountsPro
PYTHONEasy48Payment Method Type SummaryPro
PYTHONMedium49Merchant Category BreakdownPro
PYTHONMedium50Captured USD ChargesPro
PYTHONEasy51High-Value Captured ChargesPro
PYTHONMedium52Failed Charges by Failure CodePro
PYTHONMedium53Delinquent Customers With Card PaymentsPro
PYTHONHard54Charges Per CurrencyPro
PYTHONEasy55Total Revenue by MerchantPro
PYTHONMedium56Average Charge Amount by StatusPro
PYTHONMedium57Merchant Charge Stats by CategoryPro
PYTHONHard58Payout Summary by Method and StatusPro
PYTHONHard59Charges With Merchant NamesPro
PYTHONEasy60Refunds With Charge DetailsPro
PYTHONMedium61Customers Without ChargesPro
PYTHONMedium62Disputed Charges Full ContextPro
PYTHONHard63Charge With Payment Method DetailPro
PYTHONHard64Rank Merchants by Charge VolumePro
PYTHONMedium65Running Total Revenue Per MerchantPro
PYTHONMedium667-Day Moving Average Charge AmountPro
PYTHONHard67Daily Payout Volume ChangePro
PYTHONHard68Charge Authorization Hour and DayPro
PYTHONEasy69Capture Delay in MinutesPro
PYTHONMedium70Monthly Charge Volume by CurrencyPro
PYTHONHard71Fill Missing Payment Method FieldsPro
PYTHONEasy72Normalize Charge Amounts to DollarsPro
PYTHONMedium73Standardize Payment Brands to CategoriesPro
PYTHONHard74Pivot Charge Counts by Currency and StatusPro
PYTHONMedium75Balance Transaction Pivot by Source TypePro
PYTHONHard76Classify Charges by Amount TierPro
PYTHONMedium77Charge Risk ScorePro
PYTHONHard78Fee Rate and Net MarginPro
PYTHONMedium79Merchant Charge Volume QuartilePro
PYTHONHard80Time-Based Charge FeaturesPro
PYTHONHard81Merchant Feature MatrixPro
PYTHONExpert82Charge Fraud Risk FeaturesPro
PYTHONHard83Charge Amount Statistics by CurrencyPro
PYTHONMedium84Charge Amount vs Fee CorrelationPro
PYTHONHard85Anomalous Charge Detection (IQR)Pro
PYTHONHard86Merchant Health ScorecardPro
PYTHONHard87Customer Payment Profile ReportPro
PYTHONHard88Refund Impact Analysis by MerchantPro
PYTHONExpert89Dispute Resolution ReportPro
PYTHONExpert90End-to-End Payment Flow ReportPro
PYTHONExpertReady to practice Payments?
90 SQL and Python challenges built from real payments data. Graded instantly in your browser — no setup required.