Start by clarifying requirements: donation amounts, payment methods, and consistency guarantees. Then design a system that treats the payment SDK as an unreliable external dependency, using idempotency, webhooks, and a reconciliation process to handle failures and ensure data integrity.
Pro tip: Emphasize idempotency and reconciliation—these are the keys to preventing lost or duplicate donations. Also, mention that you would log every payment attempt and its outcome for auditing and debugging.
Ask about expected traffic, payment methods, and consistency requirements (e.g., eventual vs. strong). Understand the payment SDK's capabilities and limitations.
Use idempotency keys for each donation attempt to prevent duplicate charges. Implement retries with exponential backoff for transient failures, but only for idempotent operations.
Categorize failures: network errors, declined payments, and partial failures (e.g., payment succeeded but recording failed). Use webhooks to receive asynchronous payment status updates and update the database accordingly.
Implement a reconciliation job that periodically compares the payment provider's records with your database. Detect and resolve discrepancies, such as missing donations or amount mismatches.
Set up monitoring for payment success rates, failure rates, and reconciliation discrepancies. Alert on anomalies to quickly address issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a queue-based async processing model pretty quickly and they seemed to like that direction.
Start by clarifying the requirements and scale of the donation campaigns, then propose a scalable, resilient architecture that can handle sudden spikes. Focus on horizontal scaling, caching, and asynchronous processing to absorb traffic bursts, and discuss trade-offs between consistency, availability, and cost.
Pro tip: Emphasize the importance of designing for failure and having graceful degradation—donors should still be able to donate even if some non-critical services are down. Also, mention the need for real-time monitoring and auto-scaling to react quickly to spikes.
Ask questions to understand expected traffic patterns, peak load, data consistency needs, and budget constraints. This ensures your design addresses the actual problem.
Outline a scalable architecture using load balancers, stateless services, and a CDN for static assets. Consider microservices for donation processing, user management, and notifications.
Describe how to handle spikes with horizontal scaling, auto-scaling groups, and caching (e.g., Redis for session data, CDN for content). Use queue-based load leveling for write-heavy operations.
Discuss database choices (e.g., sharded SQL, NoSQL) and trade-offs between consistency and availability. Implement idempotency for donation transactions and use distributed transactions or sagas if needed.
Explain how to monitor system health with metrics and alerts, and design fallbacks (e.g., read-only mode, queueing donations) to maintain core functionality during extreme load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt more straightforward compared to the rest.
Start by clarifying requirements: who donates (riders), what they donate to (e.g., Lyft's charitable partners), and how receipts are used (tax, confirmation). Then design RESTful APIs for donation creation, history retrieval with pagination/filtering, and receipt generation (on-demand or automatic), ensuring idempotency, security, and scalability.
Pro tip: Discuss idempotency keys for donation endpoints to prevent duplicate charges, and consider asynchronous receipt generation with a signed URL to avoid blocking the user experience.
Ask about donation sources (in-app, round-up), frequency (one-time, recurring), receipt format (PDF, email), and compliance needs (tax laws, data retention).
Specify endpoints, HTTP methods, request/response schemas, and status codes for donation, history, and receipt operations, using RESTful conventions.
Incorporate idempotency, pagination, filtering, rate limiting, authentication/authorization, and error handling into the API design.
Outline database tables for donations, receipts, and users, considering indexes for efficient history queries and storage for receipt files.
Explain how the APIs integrate with payment systems, notification services, and analytics, and how they scale with caching, async processing, and sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I drew out three core tables and the relationships.
Start by clarifying the business requirements and access patterns for donations, donors, and campaigns, then propose a normalized relational schema with core entities and relationships. Discuss how you would handle scalability, data integrity, and analytical needs, and be ready to adapt the model based on specific use cases like reporting or real-time processing.
Pro tip: Demonstrate awareness of trade-offs between normalization and denormalization for read-heavy analytics, and mention how you would evolve the schema over time using migrations and versioning.
Ask questions to understand the scale, read/write patterns, and key use cases (e.g., real-time donation processing, reporting, donor segmentation). This ensures the model aligns with business needs.
Define the main entities: Donor, Campaign, Donation, and possibly Organization or Payment. Specify relationships (e.g., a donor can make many donations, a campaign has many donations).
Propose tables with primary keys, foreign keys, and essential fields (e.g., Donor: id, name, email; Campaign: id, name, goal, start_date; Donation: id, donor_id, campaign_id, amount, timestamp). Consider adding junction tables for many-to-many relationships if needed.
Discuss indexing strategies, partitioning (e.g., by date), and potential denormalization for analytics. Mention how you would handle high write throughput and large data volumes.
Talk about constraints (foreign keys, unique constraints), transactions, and how to handle schema changes over time (migrations, versioning). Also mention soft deletes and audit trails if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Observability I covered reasonably well: latency on payment processing, queue depth, error rates by failure type.
Start by defining the system's critical user journeys and fraud-sensitive flows, then propose a layered observability strategy covering metrics, logs, and traces. Focus on actionable signals that balance fraud detection with user experience, and explain how you'd iterate on thresholds and models.
Pro tip: Emphasize that observability for fraud is not just about catching bad actors but also about minimizing false positives that hurt legitimate users—show you understand the business trade-off.
Map out the system's key user journeys (e.g., signup, payment, ride booking) and pinpoint where fraud is most likely to occur, such as account takeover, promo abuse, or payment fraud.
Outline the three pillars—metrics, logs, and traces—and explain how each will be instrumented. For example, metrics for real-time monitoring, logs for detailed forensics, and traces for end-to-end request visibility.
List concrete signals to monitor, such as velocity of transactions per user, device fingerprint anomalies, IP reputation, and behavioral biometrics. Tie each signal to a fraud type.
Describe how you'd set thresholds, use anomaly detection, and route alerts to the right teams. Include automated responses like step-up authentication or temporary blocks.
Explain how you'd track false positive/negative rates, adjust models, and incorporate feedback loops from fraud analysts to continuously improve detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.