This felt manageable at first and then the follow-ups kept coming.
Start by clarifying requirements and scale (e.g., donation rate, peak traffic, payment API limits) to set the stage. Then design a scalable, idempotent donation pipeline with a focus on data integrity and reconciliation, given the manual check-writing process. Finally, discuss trade-offs and potential improvements like automated payouts.
Pro tip: Emphasize idempotency and reconciliation from the start—this shows you understand the criticality of financial transactions and the risks of manual processes. Also, mention how you'd handle the third-party payment API's rate limits and failures gracefully.
Ask questions to understand expected donation rate, peak load, payment API constraints, and charity payout process. Confirm that all funds go to one account and payouts are manual.
Outline the end-to-end flow: user initiates donation, system processes payment via third-party API, records transaction, and updates charity totals. Ensure idempotency to prevent duplicate charges.
Propose a scalable architecture (e.g., load balancers, stateless services, message queues) to handle millions of donations. Include retry mechanisms, circuit breakers, and fallbacks for payment API failures.
Design a ledger system with immutable transaction logs. Implement reconciliation between internal records and payment API reports, and provide tools for finance to verify totals before writing checks.
Acknowledge limitations of manual payouts and suggest potential automation. Discuss trade-offs between consistency and availability, and how to handle edge cases like refunds or chargebacks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to async processing and a queue in front of the payment API calls.
Start by clarifying the expected scale and traffic patterns, then propose a multi-layered strategy that combines proactive capacity planning, elastic auto-scaling, and graceful degradation. Emphasize trade-offs between consistency, availability, and cost, and tie your answer back to DoorDash's real-time, location-based delivery system.
Pro tip: Mention that you would simulate the spike with load tests and game days, and pre-scale based on historical data—this shows you think about prevention, not just reaction.
Ask about expected traffic volume, peak QPS, latency SLOs, and critical user journeys (e.g., order placement, tracking). This ensures your design targets the right bottlenecks.
Propose horizontal scaling with auto-scaling groups, load balancers, and stateless services. Use multi-AZ deployments and caching (e.g., Redis, CDN) to absorb read-heavy traffic.
Prioritize essential flows (e.g., checkout) and shed non-critical load (e.g., recommendations). Use rate limiting, circuit breakers, and queues to prevent cascading failures.
Discuss database sharding, read replicas, and eventual consistency for non-critical data. Consider NoSQL for high write throughput and caching for hot data.
Set up real-time monitoring and alerting. Conduct load tests and chaos experiments to validate the system's behavior under spike conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and data sources, then describe a systematic reconciliation process that compares internal records with provider reports, identifies discrepancies, and resolves them. Emphasize automation, idempotency, and auditability to ensure accuracy and scalability.
Pro tip: Mention the importance of using a unique transaction identifier and handling timezone differences, as these are common pitfalls in payment reconciliation. Also, highlight the need for a reconciliation dashboard to monitor discrepancies over time.
Identify which internal systems (e.g., donation service, database) and external provider reports (e.g., Stripe, PayPal) are involved. Clarify the time range and transaction types to reconcile.
Pull internal donation records and provider transaction reports. Normalize fields such as transaction ID, amount, currency, timestamp, and status to enable accurate comparison.
Join records on a unique transaction ID (or a composite key). Categorize discrepancies: missing internally, missing externally, amount mismatches, status mismatches, and duplicates.
For each discrepancy type, determine root cause (e.g., timing issues, failed webhooks, currency conversion errors). Apply fixes such as reprocessing, manual adjustments, or updating internal records.
Implement a scheduled reconciliation job with alerting for discrepancies. Build a dashboard to track reconciliation metrics and ensure ongoing accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a status enum on the donation row plus a separate events table for state transitions.
Start by clarifying the business requirements and constraints, then propose a schema that separates charges, refunds, and failures into distinct entities with clear relationships. Emphasize trade-offs between normalization, query performance, and auditability, and discuss how the design supports idempotency and reconciliation.
Pro tip: Demonstrate awareness of real-world payment complexities like partial refunds, multiple refund attempts, and chargebacks, and explain how your schema handles these without data duplication or inconsistency.
Ask about expected query patterns, data volume, consistency needs, and integration with payment providers. This shows you tailor the schema to actual use cases.
Define entities like Charge, Refund, and Failure, and their relationships (e.g., one charge can have multiple refunds). Consider whether failures are separate entities or states of a charge.
Propose tables/collections with fields, keys, and indexes. Discuss normalization vs. denormalization, and how to handle partial refunds and multiple failures.
Explain how to prevent duplicate charges/refunds using idempotency keys and how to maintain an audit trail for reconciliation and debugging.
Mention how the schema supports high write throughput, archival, and future changes like new payment methods or refund types.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the manual process and the need for an audit trail, then propose a data model that captures each check issuance with immutable records, including who, what, when, and why. Discuss how to ensure integrity, traceability, and compliance, and how the system can support reconciliation and reporting.
Pro tip: Emphasize that even manual steps should be logged automatically where possible, and that the audit trail should be designed to be tamper-evident and easily auditable by internal and external parties.
Ask questions to understand the manual check issuance workflow: who initiates, who approves, what data is captured, and what systems are involved.
Identify what needs to be recorded for compliance, reconciliation, and dispute resolution, such as check details, approver identity, timestamps, and supporting documents.
Propose a schema for audit records that includes fields like check ID, amount, payee, issuer, timestamp, status, and links to related events or documents.
Discuss mechanisms to make the audit trail immutable and verifiable, such as append-only logs, cryptographic hashing, and access controls.
Explain how the audit trail can be queried to reconcile payments, generate reports, and provide evidence during audits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept it simple: aggregate queries on the donations table grouped by charity ID, filtered by status = 'succeeded'.
Start by clarifying the requirements: what metrics are needed per charity, what is the expected scale, and what are the latency and accuracy requirements. Then propose a data pipeline that captures events in real-time, aggregates them per charity, and stores them for both real-time dashboards and post-event reporting. Emphasize scalability, fault tolerance, and data consistency.
Pro tip: Mention the importance of defining a clear event schema and using a unique charity identifier across all systems to avoid data discrepancies. Also, discuss how you would handle late-arriving data and ensure exactly-once processing.
Ask questions to understand what metrics are needed (e.g., donations, orders, user engagement), the expected volume of events, and the required latency for real-time vs. batch reporting.
Propose an event-driven architecture where all relevant user actions are logged with a charity ID. Use a message queue like Kafka to ingest events reliably and scale horizontally.
Use a stream processing framework (e.g., Flink, Spark Streaming) to compute per-charity metrics in real-time. Store results in a fast-access store like Redis or a time-series database for live dashboards.
Periodically (e.g., hourly or daily) run batch jobs (e.g., Spark) over raw event data stored in a data lake (e.g., S3) to generate accurate, comprehensive reports and handle late data.
Expose aggregated data via APIs for internal and external consumers. Use a BI tool (e.g., Tableau) for post-event analysis and ensure data consistency between real-time and batch layers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.