I started with the POST /withdrawals endpoint and worked outward from there.
Start by clarifying the requirements and constraints, then design the API endpoints with proper HTTP semantics, request/response schemas, and status codes. Emphasize idempotency by using an idempotency key and ensuring the operation is safe to retry, and discuss trade-offs and edge cases.
Pro tip: Demonstrate awareness of financial regulations and security best practices, such as using HTTPS, OAuth 2.0, and audit logging. Also, mention how you would handle partial failures and reconciliation in a distributed system.
Ask questions to understand the scope: authentication method, supported destination types, withdrawal limits, and compliance requirements. This shows you think before coding.
Define the endpoint (e.g., POST /loans/{loanId}/withdrawals), choose the appropriate HTTP verb, and specify request/response schemas. Include headers for idempotency and authentication.
Outline success and error responses with appropriate HTTP status codes (e.g., 201 Created, 400 Bad Request, 401 Unauthorized, 409 Conflict, 422 Unprocessable Entity). Describe error response structure.
Explain how to use an idempotency key (e.g., in the request header) to ensure that retrying the same request does not create duplicate withdrawals. Discuss storage and expiration of keys.
Address topics like concurrency, race conditions, partial failures, and how to handle asynchronous processing if needed. Mention monitoring and auditing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pending, approved, disbursed, failed, reversed.
Start by clarifying the scope and requirements of the withdrawal process, then define a clear state machine with well-defined states and transition triggers. Finally, design a RESTful endpoint that allows querying the current status, considering idempotency, security, and scalability.
Pro tip: Emphasize the importance of idempotent transitions and audit logging to ensure reliability and compliance, especially in a financial context like SoFi.
Ask questions to understand the withdrawal process, including actors, business rules, and non-functional requirements like latency and consistency.
Enumerate the possible states (e.g., PENDING, PROCESSING, COMPLETED, FAILED, CANCELLED) and specify what triggers each transition, including timeouts and manual interventions.
Propose a schema to persist withdrawal requests and their state history, ensuring auditability and efficient querying.
Define a RESTful endpoint (e.g., GET /withdrawals/{id}) that returns the current status and relevant metadata, with proper error handling and security.
Discuss handling concurrent updates, idempotency, and scaling the endpoint for high read volume.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the withdrawal lifecycle and cancellation requirements, then propose a design that uses idempotent APIs and state transitions to safely cancel pending withdrawals. Walk through edge cases like race conditions, partial processing, and failure recovery, explaining how you'd handle each with appropriate mechanisms.
Pro tip: Emphasize idempotency and state machine design early—this shows you think about reliability and consistency, which is critical in fintech. Also, mention that you'd log cancellation attempts and outcomes for audit and debugging.
Ask about the withdrawal process: what states exist (e.g., pending, processing, completed), what triggers cancellation, and any regulatory or timing constraints. Confirm whether cancellation is user-initiated or system-initiated.
Propose an idempotent API endpoint (e.g., POST /withdrawals/{id}/cancel) that transitions the withdrawal to a 'cancelled' state if allowed. Define a state machine with clear allowed transitions and use optimistic locking or versioning to handle concurrency.
Explain how to prevent double-cancellation or cancellation after processing starts. Use database transactions with row-level locks or compare-and-swap operations to ensure atomic state changes.
Cover scenarios like cancellation after funds are reserved but before transfer, partial withdrawals, network failures during cancellation, and idempotency keys to safely retry. Discuss how to reconcile with external payment systems.
Describe logging, metrics, and alerts for cancellation attempts and failures. Mention the need for an audit trail to track who cancelled what and when, which is essential for compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cursor-based over offset, I said it immediately and explained why offset breaks on large tables with frequent inserts.
Start by clarifying the requirements: expected data volume, access patterns, and consistency needs. Then recommend cursor-based pagination using a stable, unique key (e.g., withdrawal ID or timestamp+ID) to ensure efficient and consistent results. Explain why offset pagination is problematic for large, frequently updated datasets, and discuss trade-offs like complexity and client support.
Pro tip: Mention that cursor-based pagination avoids the 'page drift' problem where new withdrawals shift results, and that you'd encode the cursor as an opaque token to allow future changes without breaking clients.
Ask about data volume, update frequency, and client needs (e.g., jump to page, infinite scroll). This determines the appropriate pagination strategy.
Compare offset vs. cursor-based pagination. Offset is simple but inefficient and inconsistent for large, changing datasets; cursor-based is efficient and stable.
Choose a unique, sequential field (e.g., withdrawal ID or created_at + ID) and encode it as an opaque cursor. Ensure it's stable and sortable.
Specify query parameters (e.g., limit, cursor) and response structure (data array, next_cursor). Include error handling for invalid cursors.
Discuss handling of deleted records, ensuring index usage, and potential caching. Mention that cursor-based pagination works well with database indexes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then propose a layered approach combining database transactions, locking strategies, and application-level checks. Emphasize the trade-offs between consistency, performance, and scalability, and mention how you would handle failures and concurrency.
Pro tip: Demonstrate awareness of real-world constraints by discussing how you would monitor and test for race conditions, and mention that you would consider using optimistic locking with retries for high-throughput scenarios.
Ask about expected concurrency levels, latency requirements, and whether the system is distributed. This shows you understand the problem context before jumping to solutions.
Discuss options like pessimistic locking (SELECT FOR UPDATE), optimistic locking (version numbers), or serializable isolation. Explain when each is appropriate based on contention and performance needs.
Ensure the check (balance >= withdrawal) and the update (deduct balance) occur within a single atomic transaction. Use database transactions with appropriate isolation levels to prevent race conditions.
Describe how to handle deadlocks, lock timeouts, and optimistic lock failures with retries or fallback strategies. Mention idempotency to avoid duplicate withdrawals.
If the system is distributed, discuss using distributed locks, consensus algorithms, or event sourcing. Highlight trade-offs between strong consistency and availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I described publishing a withdrawal-requested event after the DB write commits, with a downstream settlement service consuming it and writing back status updates.
Start by clarifying the settlement flow's requirements, including regulatory constraints like audit trails and idempotency. Then propose an event-driven architecture with a durable event bus, emphasizing patterns for exactly-once processing, event sourcing, and compliance. Conclude by discussing trade-offs and how you'd ensure auditability and regulatory adherence.
Pro tip: Mention specific regulations like SOX, PCI-DSS, or GDPR and how they influence design choices (e.g., immutable logs, data retention). Also, highlight the importance of idempotent consumers and dead-letter queues to handle failures without data loss.
Ask about settlement volume, latency, regulatory requirements, and existing systems. Confirm the need for auditability and compliance.
Propose a durable event bus (e.g., Kafka) with topics for settlement events. Ensure events are immutable and persisted for audit.
Use idempotent consumers, deduplication, and transactional outbox patterns to avoid duplicate settlements and ensure consistency.
Store events in an append-only log, enable event replay for auditing, and enforce data retention policies. Integrate with monitoring and alerting.
Address trade-offs like latency vs. consistency, and describe dead-letter queues, retries, and compensation for failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rate limiting per user token with a token bucket, and a stricter limit on the POST endpoint vs the GET ones.
Start by clarifying the API's purpose, expected traffic patterns, and criticality to the business. Then propose a layered rate limiting strategy using algorithms like token bucket or sliding window, and outline observability with metrics, logging, and tracing. Emphasize trade-offs between protection, user experience, and system complexity.
Pro tip: Tie rate limiting and observability to business metrics like conversion rates and support tickets, showing you understand the product impact. Also, mention that rate limits should be configurable and observable themselves to avoid becoming a silent failure point.
Ask about expected traffic volume, user tiers, SLA requirements, and whether the API is public or internal. This ensures your strategy aligns with business needs.
Choose appropriate algorithms (e.g., token bucket, leaky bucket, fixed/sliding window) and define limits per user, IP, or API key. Consider distributed rate limiting using Redis or a dedicated service.
Instrument the API with metrics (request rate, latency, error rates, rate limit hits), structured logging, and distributed tracing. Use tools like Prometheus, Grafana, ELK, or OpenTelemetry.
Set up alerts for anomalies like sudden traffic spikes or increased 429 responses. Create dashboards for real-time monitoring and capacity planning.
Acknowledge trade-offs: strict limits may frustrate users, while loose limits risk abuse. Propose starting with conservative limits and iterating based on observed data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.