The core model felt straightforward until I started thinking about where to compute the totals.
Start by clarifying requirements and constraints, then design a data model that separates line items, invoices, and payments to ensure accurate totals and status updates. Implement the core logic with attention to monetary precision, idempotency, and state transitions, and expose a clean API for recording payments and retrieving invoice status.
Pro tip: Use integer cents for all monetary calculations to avoid floating-point errors, and make payment recording idempotent with a client-supplied idempotency key—this mirrors Stripe's own API design and shows production maturity.
Ask about expected scale, currency handling, tax rules (inclusive/exclusive), payment methods, and whether partial payments or refunds are needed. Confirm the API surface and any constraints like idempotency or concurrency.
Define entities: Invoice (id, status, subtotal, tax, total, amount_paid, balance), LineItem (description, quantity, unit_price, tax_rate), and Payment (id, invoice_id, amount, method, timestamp). Use integer cents for money and store tax rate as a decimal fraction.
Compute line item totals as quantity * unit_price, subtotal as sum of line totals, tax as sum of (line_total * tax_rate), and total as subtotal + tax. Derive payment status from amount_paid vs total: unpaid, partially_paid, paid, or overpaid.
Expose endpoints like POST /invoices, GET /invoices/{id}, and POST /invoices/{id}/payments. Ensure payment recording is idempotent (via idempotency key) and updates the invoice status atomically, handling concurrent payments with optimistic locking or transactions.
Address rounding, multiple currencies, tax-inclusive pricing, partial payments, refunds, and audit trails. Mention how to scale (e.g., event sourcing, ledger) and ensure consistency across distributed systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: whether rounding should occur at the line-item level or on the total, and what rounding mode (e.g., half-up, banker's) is expected. Then propose a strategy that minimizes cumulative error, such as using integer arithmetic (e.g., cents) and applying rounding only at the final step, while ensuring consistency with accounting standards. Finally, discuss trade-offs between simplicity, accuracy, and performance, and mention how you would test and validate the approach.
Pro tip: Mention that Stripe's API often expects amounts in the smallest currency unit (e.g., cents) and that you should avoid floating-point arithmetic entirely; use integers or decimal libraries to prevent precision loss.
Ask whether rounding should be applied per line item or on the total, and what rounding rule (e.g., half-up, half-even) is required by business or regulatory constraints.
Decide between rounding each line item then summing, or summing exact values then rounding once. Consider using the largest remainder method to distribute rounding differences fairly.
Use integer arithmetic (e.g., cents) or a decimal library to avoid floating-point errors. Apply rounding only at the final step if possible.
Write unit tests for edge cases (e.g., .005, multiple items with fractional cents) and verify that the sum of rounded line items matches the rounded total when required.
Explain the trade-offs between per-line rounding (simpler but can cause total mismatch) and total rounding (more accurate but may require distributing adjustments).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the API contracts and business rules for invoices and payments, then systematically cover validation layers: syntactic (types, formats), semantic (business logic, referential integrity), and security (authorization, idempotency). Emphasize trade-offs between strict validation and flexibility, and mention how validation errors should be returned consistently.
Pro tip: Show awareness of Stripe's API design principles: use idempotency keys for payment recording to prevent duplicate charges, and validate amounts against currency-specific constraints (e.g., zero-decimal currencies). Also, consider validation at the edge (API gateway) vs. service layer to balance performance and maintainability.
Ask about the API specification (e.g., OpenAPI), expected clients, and business rules. Understand what fields are required, optional, and their constraints.
Check data types, required fields, string lengths, patterns (e.g., email, currency codes), and numeric ranges. Use JSON Schema or similar for declarative validation.
Validate that referenced entities exist (e.g., customer, invoice), amounts are positive and within limits, currencies match, and state transitions are allowed (e.g., cannot pay a voided invoice).
Ensure the caller is authorized, validate API keys/scopes, and require idempotency keys for payment creation to prevent duplicates. Sanitize inputs to prevent injection attacks.
Return consistent, actionable error responses (e.g., 400 with error codes and messages). Log validation failures for monitoring and debugging without exposing sensitive data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the most interesting part of the discussion.
Start by clarifying the context—what kind of overpayment, who is involved, and what the business goals are. Then evaluate trade-offs between rejecting outright and alternative handling, considering factors like user experience, financial risk, and operational cost. Finally, propose a nuanced solution that balances these factors, possibly with conditional rules or a hybrid approach.
Pro tip: Demonstrate product sense by considering the user's perspective: an overpayment might be a mistake, but rejecting it outright could frustrate a customer and lead to churn. Instead, propose a solution that automatically refunds or credits the excess, while flagging for review if suspicious.
Ask questions to understand the type of overpayment (e.g., customer pays too much, system error, duplicate payment) and the business context (e.g., one-time vs. recurring, B2B vs. B2C).
Consider the impact on customers, finance, operations, and engineering. Define what success looks like: minimizing friction, reducing manual work, preventing fraud, etc.
Compare rejecting outright vs. alternatives like auto-refund, credit, or manual review. Assess pros and cons for each stakeholder and the system.
Recommend a primary approach (e.g., auto-refund for small amounts, manual review for large) and explain how it aligns with business goals and technical feasibility.
Discuss how to implement (e.g., API design, idempotency, notifications) and handle edge cases like partial overpayments, currency issues, or regulatory constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.