Start by clarifying functional and non-functional requirements, then sketch a high-level architecture covering core services like campaign management, donation processing, and payout. Dive into critical components such as payment integration, recurring billing, and data consistency, discussing trade-offs and scalability.
Pro tip: Emphasize idempotency and exactly-once processing for donations to prevent double-charging, and discuss how you'd handle reconciliation between internal ledgers and external payment providers.
Ask questions to understand scope: expected scale (users, campaigns, donation volume), payment methods, currencies, compliance needs (PCI, KYC), and whether real-time progress tracking is required.
Outline main components: API gateway, user service, campaign service, donation service, payment service, notification service, and payout service. Describe data flow for browsing, donating, and receiving payouts.
Detail donation processing: idempotent APIs, integration with payment gateways (e.g., Stripe), handling recurring donations via scheduler, and updating campaign progress with eventual consistency.
Propose schemas for users, campaigns, donations, receipts, and payouts. Discuss database choices (SQL for transactions, NoSQL for scale) and how to ensure ACID for financial data.
Address scaling reads/writes, caching campaign data, sharding strategies, and trade-offs between consistency and availability. Discuss failure handling, retries, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew to say integer arithmetic in the smallest currency unit (cents, not dollars) and they seemed fine with that.
Start by acknowledging that financial calculations require exact decimal arithmetic, not floating-point. Then describe a ledger design that stores amounts in minor units (e.g., cents) as integers, uses double-entry bookkeeping for consistency, and applies rounding rules only at the final aggregation step. Finally, discuss how to handle multi-currency and reconciliation to ensure correctness across the donation ledger.
Pro tip: Mention that you would use a library like decimal.js or Java's BigDecimal for any intermediate calculations, and that you would enforce idempotency and audit trails to catch discrepancies early. This shows you understand both the technical and business-critical nature of money math.
Use integer minor units (e.g., cents) or a decimal library to avoid floating-point errors. Never use float or double for monetary values.
Model the donation ledger as a double-entry system where every transaction has balanced debits and credits, ensuring consistency and auditability.
Specify when rounding occurs (e.g., at the final step) and how to allocate remainders (e.g., largest remainder method) to avoid losing cents.
Add checks to verify that sums match expected totals, and run periodic reconciliation jobs to detect and correct discrepancies.
Store currency codes with amounts, convert using exact rates, and ensure operations are idempotent to prevent duplicate entries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about batching payouts and reconciling against the ledger before disbursing.
Start by clarifying requirements (scale, currencies, payout frequency, charity onboarding) and then present a high-level design covering data model, payout orchestration, and integration with payment providers. Walk through the end-to-end flow from donation capture to charity disbursement, highlighting trade-offs like batching vs. real-time, idempotency, and reconciliation.
Pro tip: Emphasize idempotency and reconciliation from the start—interviewers at DoorDash care deeply about correctness and financial integrity, so showing you prioritize these will set you apart.
Ask about scale (donations per day, number of charities), payout frequency (daily, weekly, threshold-based), currencies, and regulatory constraints. This ensures your design addresses the right problems.
Outline the main components: donation service, ledger, payout service, charity accounts, and payment provider integration. Describe how donations are recorded and funds are earmarked for payouts.
Explain how payouts are triggered (scheduled or threshold-based), how batches are formed, and how the payout service interacts with payment providers. Discuss idempotency keys and retry mechanisms.
Detail the ledger design to track balances per charity, including double-entry accounting for accuracy. Mention how to handle refunds, chargebacks, and adjustments.
Describe how you reconcile internal records with payment provider reports, handle discrepancies, and monitor for failures. Include alerting and audit trails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with horizontal scaling on the campaign read path, caching campaign progress so you're not hammering the DB on every page load, and async writes for donation counts.
Start by clarifying the scenario and requirements, then walk through your architecture layer by layer, explaining how each component scales and handles failures. Emphasize trade-offs between consistency, availability, and cost, and tie your answer back to DoorDash's specific needs like order processing and delivery logistics.
Pro tip: Show that you think about the entire lifecycle of a traffic spike—not just scaling up, but also scaling down gracefully to avoid over-provisioning and cost overruns. Mention how you'd use load testing and chaos engineering to validate your design before a real spike hits.
Ask questions to understand the expected scale, traffic patterns, latency requirements, and consistency needs. State your assumptions clearly before diving into the design.
Briefly describe the main components (e.g., load balancers, API gateways, microservices, databases, caches, queues) and how they interact. Keep it concise to set the stage.
Explain how each layer handles spikes: horizontal scaling with auto-scaling groups, caching at multiple levels, asynchronous processing with queues, database read replicas and sharding, and rate limiting.
Discuss trade-offs (e.g., consistency vs. availability, cost vs. performance) and how you handle failures (circuit breakers, retries with backoff, graceful degradation).
Describe how you'd monitor the system (metrics, logging, tracing), load test to find bottlenecks, and iterate on the design based on real-world feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rushed this because we were running low on time.
Start by clarifying the business goals behind donation flows—such as increasing donor conversion, retention, and average gift size—then propose a set of analytics that track the full funnel from impression to repeat donation. For data modeling, outline an event-based schema that captures donor, campaign, and transaction entities, and explain how you'd handle slowly changing dimensions and real-time aggregation.
Pro tip: Emphasize the importance of defining clear, actionable metrics and ensuring data quality through validation and monitoring; this shows you understand that analytics must drive decisions, not just report numbers.
Ask about the specific goals of the donation feature, such as increasing conversion rates, donor retention, or average donation amount, to ensure your analytics align with business value.
Identify the key performance indicators (KPIs) across the donation funnel: impressions, click-through rate, donation start rate, completion rate, average gift size, and repeat donation rate.
Propose an event-based schema with core entities: Donor, Campaign, Donation, and Event. Include slowly changing dimensions for donor attributes and consider a star schema for analytical queries.
Explain how data would be ingested (e.g., via Kafka), processed (e.g., Spark), and stored (e.g., in a data warehouse like Snowflake) to support both batch and real-time analytics.
Describe how you would build dashboards and reports for stakeholders, and how you would enable ad-hoc analysis and A/B testing to optimize donation flows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.