I spent too long on the requirements phase and the interviewer had to nudge me to move on.
Start by clarifying the scope and constraints of the 3-day campaign model, then systematically cover functional requirements, non-functional goals, and traffic estimates before proposing a high-level architecture. Focus on the unique challenges of short-lived, high-intensity campaigns and how they influence design decisions.
Pro tip: Emphasize the importance of handling traffic spikes and ensuring data consistency during campaign creation and donation processing, as these are critical for a donation platform with time-bound campaigns. Also, discuss how you would monitor and adapt the system in real-time during a campaign.
Ask clarifying questions to understand the platform's scope, such as expected user base, campaign creation process, payment methods, and any regulatory constraints. State your assumptions explicitly.
List core features: campaign creation (with 3-day duration), donation processing, user authentication, campaign discovery, real-time progress tracking, and notifications. Prioritize must-have vs. nice-to-have features.
Discuss scalability (handle traffic spikes), availability (99.9% uptime), latency (sub-second response for donations), consistency (accurate donation totals), and security (PCI compliance, fraud prevention).
Estimate daily active users, peak concurrent users, donations per second, and storage needs for campaign data and media. Use back-of-the-envelope calculations to justify your numbers.
Sketch a scalable architecture using load balancers, stateless services, caching, message queues, and a sharded database. Highlight how components handle the 3-day campaign lifecycle and traffic spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's core use cases and constraints, then define the primary entities and their relationships. Design APIs around key workflows, ensuring idempotency and clear separation of concerns. Discuss trade-offs and scalability considerations.
Pro tip: Demonstrate maturity by explicitly addressing idempotency for payment operations and data consistency across services, showing you understand real-world payment system challenges.
Ask questions to understand the system's purpose, scale, and key workflows (e.g., donation processing, campaign management). Identify non-functional requirements like consistency, latency, and idempotency.
Model Campaign, Donation, Donor, PaymentIntent, and Receipt with their attributes and relationships. Consider normalization vs. denormalization and how entities interact.
Define RESTful or RPC-style endpoints for creating campaigns, making donations, processing payments, and generating receipts. Ensure APIs are idempotent and support pagination and filtering.
Discuss how to maintain consistency across services (e.g., using transactions, sagas, or event sourcing) and how to scale reads/writes (e.g., sharding, caching).
Highlight trade-offs in your design (e.g., SQL vs. NoSQL, synchronous vs. asynchronous processing) and suggest potential extensions like webhooks or analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on reconciliation specifically.
Start by clarifying the payment flow and requirements, then walk through the lifecycle of a payment from initiation to reconciliation. Emphasize idempotency, retries with backoff, and reconciliation as critical components, and discuss trade-offs between consistency and availability.
Pro tip: Mention that idempotency keys should be generated client-side and stored server-side with a unique constraint, and that reconciliation should be automated with alerts for mismatches to avoid financial discrepancies.
Ask questions to understand the payment flow, expected volume, and consistency requirements. Outline the end-to-end process from payment initiation to settlement.
Explain how to use idempotency keys to ensure that duplicate requests don't result in double charges. Discuss key generation, storage, and expiration.
Describe retry strategies with exponential backoff and jitter, and how to handle different failure scenarios (e.g., network errors vs. provider errors).
Detail how to reconcile internal records with the payment provider's reports, including scheduled jobs, mismatch detection, and resolution workflows.
Highlight trade-offs between consistency and availability, and the importance of monitoring, alerting, and logging for payment operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to rate limiting and velocity checks, which was fine but a bit shallow.
Start by clarifying the types of fraud and abuse relevant to DoorDash (e.g., payment fraud, promo abuse, account takeover) and the trade-offs between user experience and security. Then outline a layered, data-driven mitigation strategy covering prevention, detection, and response, emphasizing scalable system design and continuous improvement.
Pro tip: Highlight the importance of cross-functional collaboration with risk, legal, and data science teams, and propose a feedback loop where false positives are analyzed to refine models—showing you understand both technical and business aspects.
Enumerate common fraud and abuse types on a delivery platform (e.g., fake accounts, promo code abuse, payment fraud, refund abuse) and quantify their impact on revenue, user trust, and operations.
Propose multiple layers: prevention (e.g., identity verification, rate limiting), detection (e.g., ML models, rule engines), and response (e.g., account suspension, manual review). Explain how layers complement each other.
Describe how to use historical data to train models for anomaly detection and risk scoring, and how to continuously update models with new fraud patterns. Mention feature engineering and real-time scoring.
Discuss trade-offs: too strict measures may block legitimate users, too lenient may increase fraud. Propose strategies like risk-based authentication, gradual friction, and A/B testing to optimize.
Outline monitoring dashboards for key metrics (e.g., fraud rate, false positive rate), incident response playbooks, and a feedback loop for model retraining. Emphasize scalability and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: real-time donation totals need to be accurate and up-to-date, but slight staleness might be acceptable. Then propose a consistency model that balances correctness and availability, such as eventual consistency with read-your-writes for donors, and discuss how to handle traffic spikes using caching, sharding, and asynchronous processing.
Pro tip: Mention that you would use a hybrid approach: strong consistency for the donation transaction itself (to prevent double-counting) and eventual consistency for the displayed totals, with a short TTL cache to absorb read spikes. This shows you understand the trade-offs between consistency and scalability.
Ask about the expected scale, latency requirements, and whether slight staleness in totals is acceptable. Determine if donors need to see their own donation reflected immediately.
Propose eventual consistency for the aggregate totals to ensure high availability and partition tolerance, but use read-your-writes consistency for a donor's own donation to provide immediate feedback.
Describe how donations are written to a durable store (e.g., SQL with ACID) and then asynchronously propagate to a cache or read-optimized store for fast reads. Use idempotent writes to avoid double-counting.
At launch and close, traffic spikes can be handled by caching the totals with a short TTL, sharding the donation processing, using a queue to buffer writes, and autoscaling the read layer.
Discuss monitoring consistency lag and system health, and be prepared to adjust consistency levels dynamically (e.g., during spikes, relax to eventual consistency for totals).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly not my strongest area and it showed.
Start by clarifying the scope: which systems handle card payments, PII, and user data across regions. Then propose a defense-in-depth architecture that segments data by sensitivity, enforces least privilege, and automates compliance controls like encryption, tokenization, and audit logging. Finally, discuss trade-offs between security, performance, and cost, and how you'd validate compliance through continuous monitoring and audits.
Pro tip: Emphasize that compliance is not a one-time project but an ongoing process—show you understand the need for automated policy enforcement and regular audits. Also, mention that you'd involve legal and security teams early to avoid rework.
Identify what data is collected, where it's stored, and how it moves. Determine which regulations apply (PCI for card data, CCPA/GDPR for personal data) and the specific obligations like data subject rights and breach notifications.
Propose storing only necessary data, anonymizing or pseudonymizing where possible, and isolating sensitive data (e.g., cardholder data in a separate PCI zone) to reduce scope and risk.
Detail encryption at rest and in transit, tokenization for card data, strict access controls (RBAC, least privilege), and comprehensive logging/monitoring for audit trails.
Design mechanisms for user consent management, data access/portability, deletion (right to be forgotten), and opt-out of sale (CCPA), ensuring they work across distributed systems.
Propose automated compliance checks, regular penetration testing, audits, and a process for staying updated with regulatory changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Standard observability answer: metrics on donation throughput and payment failure rates, structured logs, distributed tracing across the payment flow.
Structure your answer around the three pillars: observability, disaster recovery, and data backfill. For each, describe specific tools, metrics, and processes you would use, and tie them to the system's critical components. Emphasize proactive measures and automation to minimize downtime and data loss.
Pro tip: Demonstrate maturity by discussing trade-offs between consistency and availability during recovery, and by mentioning how you would validate backfilled data to ensure correctness.
Outline how you would monitor the system: metrics (latency, error rates, throughput), logging (structured logs, log aggregation), tracing (distributed tracing), and alerting (thresholds, anomaly detection). Mention specific tools like Prometheus, Grafana, ELK, Jaeger.
Describe your approach to DR: backup strategies (frequency, retention), replication (multi-region, synchronous vs asynchronous), failover mechanisms (automatic vs manual), and recovery time/point objectives (RTO/RPO).
Explain how you would backfill missing or corrupted data: identify the gap, source of truth, backfill method (batch job, streaming), validation (checksums, reconciliation), and rollback plan if issues arise.
Discuss how you would test DR and backfill procedures: regular DR drills, chaos engineering, canary backfills, and monitoring during backfill to catch issues early.
Mention post-mortems, runbooks, and automation to improve future incident response and reduce manual effort.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Asked near the end when I was running low on energy.
Start by clarifying the requirements and constraints of recurring donations and corporate matching, then propose a high-level architecture that integrates with the existing payment and user systems. Focus on data model changes, idempotent payment scheduling, and third-party integrations, while discussing trade-offs like consistency, scalability, and failure handling.
Pro tip: Emphasize idempotency and reconciliation to avoid double-charging or missed matches, and mention how you'd leverage DoorDash's existing infrastructure (e.g., payment service, user profiles) to minimize new components.
Ask questions to understand scope: frequency of recurring donations, corporate matching rules (e.g., match ratios, caps), user roles, and compliance needs. This ensures you design for the right use cases.
Outline the main components: a donation service to handle one-time and recurring payments, a scheduler for recurring charges, a matching service to process corporate matches, and integration with existing user and payment systems.
Define schemas for recurring donation plans, payment schedules, corporate matching agreements, and transaction logs. Consider using a relational DB for consistency and a job queue for scheduling.
Detail the flow for setting up a recurring donation, processing a charge, handling failures (e.g., retries, dunning), and applying corporate matches. Address idempotency, race conditions, and reconciliation.
Discuss trade-offs: synchronous vs. asynchronous processing, consistency vs. availability, and build vs. buy for matching logic. Explain how the design scales with increasing donations and corporate partners.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.