Start by clarifying functional and non-functional requirements, then design a high-level architecture with core services (pricing, payments, payouts, promotions, settlement) and a data model that captures orders, transactions, and ledgers. Dive into key flows like order pricing and end-of-order settlement, discussing trade-offs around consistency, idempotency, and scalability.
Pro tip: Emphasize idempotency and exactly-once processing for financial transactions, and discuss how you'd handle partial failures and reconciliation to ensure correctness. Show awareness of regulatory and audit requirements, which is crucial for a fintech like Rippling.
Ask questions to understand scope: payment methods, currencies, tax handling, promotion types, driver payment models (per delivery, hourly, tips), restaurant payout schedules, and settlement frequency. Identify non-functional needs like consistency, latency, and auditability.
Propose a microservices architecture with separate services for order management, pricing, promotions, payments, payouts, and settlement. Use event-driven communication (e.g., Kafka) for asynchronous processing and a relational database for transactional integrity.
Design core entities: Order, OrderItem, Promotion, Payment, Payout, LedgerEntry, and Settlement. Use a double-entry ledger to track money movement between customers, drivers, restaurants, and the platform, ensuring auditability and consistency.
Walk through order pricing (applying promotions, taxes, fees), driver payment calculation, restaurant payout, and end-of-order settlement. Discuss trade-offs: synchronous vs. asynchronous processing, strong vs. eventual consistency, and how to handle failures with retries and idempotency.
Address scaling: partitioning by order ID or region, using queues to handle spikes, and implementing idempotent APIs. Discuss monitoring, alerting, and reconciliation jobs to detect and resolve discrepancies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Basically asking how you'd prove the ledger is correct.
Start by clarifying the system's money flow model and settlement process, then propose a multi-layered testing strategy that includes unit tests for individual ledger entries, integration tests for end-to-end flows, and reconciliation checks to ensure the sum of all accounts equals zero. Emphasize the importance of invariants, idempotency, and handling edge cases like refunds, chargebacks, and partial settlements.
Pro tip: Highlight that in financial systems, testing should not only verify correctness but also detect anomalies over time; suggest implementing continuous reconciliation with alerting to catch discrepancies in production. Also, mention the need to test with realistic data volumes and concurrency to uncover race conditions.
Map out all entities (customer, restaurant, driver, platform) and the transactions between them, including credits, debits, fees, refunds, and adjustments. Identify the ledger structure and how settlement aggregates these flows.
Establish the core invariant: for any settlement period, the sum of all account balances (customer, restaurant, driver, platform) must equal zero. Define expected balances for each entity based on business rules.
Write unit tests for individual ledger operations (e.g., payment, refund) to ensure they correctly debit/credit accounts. Then create integration tests that simulate complete flows from order to settlement, verifying the zero-sum invariant.
Include tests for partial settlements, refunds, chargebacks, failed transactions, and concurrent operations. Verify that the system remains balanced and idempotent under these conditions.
Propose automated reconciliation jobs that run periodically to check the zero-sum invariant across all accounts, with alerts for discrepancies. Suggest using property-based testing to generate random valid transaction sequences and assert balance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a strategy pattern for promotions and a pluggable fee calculator interface.
Start by clarifying the current system's design and the specific extension points for promotions and fee schedules. Then propose a modular architecture using patterns like strategy or plugin, and discuss how to introduce new types without modifying existing code. Finally, address trade-offs, testing, and migration strategies to ensure backward compatibility.
Pro tip: Emphasize that you would first write characterization tests to lock down existing behavior before refactoring, and mention using feature flags to safely roll out new promotion types.
Ask questions to understand the existing promotion and fee schedule logic, including how they are currently implemented and what 'breaking' means in this context.
Propose identifying stable interfaces or abstractions (e.g., Promotion interface, FeeCalculator) that can be extended without modifying existing code.
Suggest using patterns like Strategy, Factory, or Plugin to encapsulate new promotion types and fee schedules, allowing dynamic addition.
Discuss strategies like versioning, feature flags, and comprehensive testing (unit, integration, characterization) to avoid breaking existing logic.
Talk about trade-offs such as complexity vs. flexibility, performance implications, and how to handle data migration and configuration management.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.