I started with users and campaigns and then kind of free-associated from there: pledges, payments, receipts.
Start by clarifying requirements and scale, then identify core entities (User, Campaign, Donation, RecurringDonation) and their relationships. Focus on modeling one-time vs recurring donations, and discuss trade-offs like normalization vs performance.
Pro tip: Mention how you'd handle idempotency for donations and the importance of audit trails for financial transactions, showing you think about real-world reliability.
Ask about scale, payment methods, and whether recurring donations can be modified or canceled. This ensures the schema meets business needs.
List main entities: User, Campaign, Donation, RecurringDonation, and PaymentMethod. Consider if Donation should be a subtype of a Transaction entity.
Map relationships: User to Donation (one-to-many), Campaign to Donation (one-to-many), User to RecurringDonation (one-to-many), and RecurringDonation to Donation (one-to-many).
Specify primary keys, foreign keys, and indexes. For recurring donations, include fields like frequency, next_charge_date, and status.
Talk about normalization vs denormalization for reporting, handling refunds, and supporting multiple currencies. Mention potential sharding by campaign_id or user_id.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem around exactly-once processing and consistency between your system and the gateway. Then walk through the lifecycle: idempotency keys to deduplicate requests, a state machine to track payment status, and an outbox/queue pattern to reliably trigger retries and reconciliation. Emphasize trade-offs like latency vs. consistency and how you handle ambiguous failures.
Pro tip: Mention that idempotency keys must be generated and stored before the first attempt, and that you should never retry on non-idempotent operations without a key. Also highlight the importance of a reconciliation job to catch payments that succeeded on the gateway but failed locally.
Ask about scale, latency tolerance, and what 'payment failure' means (network timeout, gateway decline, etc.). Identify the need for exactly-once processing and consistency.
Explain how to generate a unique idempotency key per payment attempt, store it with the request, and pass it to the gateway. Ensure the key is reused on retries to avoid double charges.
Define states (e.g., INITIATED, PENDING, SUCCEEDED, FAILED, REFUNDED) and allowed transitions. Use this to drive retries and handle out-of-order events.
Use a transactional outbox to atomically persist payment state and enqueue retry/reconciliation messages. A queue with exponential backoff handles transient failures without blocking the main flow.
Run periodic reconciliation jobs to compare local state with gateway reports, resolving discrepancies. Monitor retry rates, latency, and error types to detect issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through a two-phase approach: write the pledge in a pending state, enqueue the payment job, then update status on callback.
Start by clarifying the requirements and constraints, such as whether the pledge and payment are in the same database or across services. Then propose a solution using distributed transactions or eventual consistency patterns, explaining trade-offs between consistency, availability, and complexity. Finally, discuss how to handle failures and ensure idempotency.
Pro tip: Mention that you would use the Saga pattern with compensating transactions for cross-service consistency, and emphasize the importance of idempotency keys to handle retries safely. This shows you understand real-world distributed system challenges.
Ask about the system architecture: are pledge and payment services separate? What consistency level is required? What are the latency and availability requirements?
Decide between strong consistency (e.g., two-phase commit) and eventual consistency (e.g., Saga pattern). Explain the trade-offs: strong consistency may impact availability and performance, while eventual consistency requires handling intermediate states.
Outline the steps: create pledge, then record payment. If using Saga, define the sequence of local transactions and compensating actions for rollback. If using 2PC, describe the prepare and commit phases.
Use idempotency keys to prevent duplicate operations on retries. Implement retry mechanisms with exponential backoff and dead-letter queues for failed compensations.
Explain how to monitor transaction states, detect stuck sagas, and provide manual intervention or automated recovery processes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Chargebacks I handled okay since I've dealt with them before.
Start by clarifying the scope: chargebacks are external disputes initiated by the customer's bank, while partial refunds are merchant-initiated. Then walk through the data model (payments, orders, ledger, disputes) and the state machine for each flow, emphasizing idempotency and consistency.
Pro tip: Mention that chargebacks and refunds must be idempotent and that you'd use an append-only ledger to track money movement, which is critical for financial correctness and auditability.
Ask whether the system handles both customer-initiated refunds and bank-initiated chargebacks, and whether partial amounts are allowed. Confirm the need for audit trails and idempotency.
List tables like orders, payments, refunds, chargebacks, and a ledger/transaction table. Explain their relationships and key fields (e.g., payment_id, amount, status).
Describe the refund lifecycle: requested → approved → processed → completed/failed. Mention how partial refunds update the original payment's refunded amount and status.
Describe the chargeback lifecycle: received → under_review → accepted (merchant loses) or disputed (merchant wins) → closed. Explain how funds are held or reversed.
Explain how you ensure exactly-once processing using idempotency keys, database transactions, and reconciliation jobs. Mention how the ledger records debits/credits for each event.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the core challenge: webhooks are at-least-once, out-of-order, and can be delayed, so you need idempotent processing and a source of truth. Then describe a state machine for payments/pledges, with webhooks as one input that triggers transitions, and reconciliation jobs to catch missed events. Emphasize how you handle duplicates, ordering, and consistency between your DB and the gateway.
Pro tip: Mention that you treat the payment gateway as the source of truth for payment status, but your internal state machine decides pledge fulfillment—and you use a periodic reconciliation job to detect and repair drift, which shows you think about failure modes beyond the happy path.
Map out the possible states for payments and pledges (e.g., pending, authorized, captured, failed, refunded) and clarify which system owns each state. The gateway owns payment status; your system owns pledge lifecycle and business rules.
Use a unique event ID from the gateway to deduplicate events, and store processed event IDs. Ensure that applying the same event multiple times results in the same state, typically via upserts or conditional updates.
Include event timestamps or sequence numbers and only apply transitions if they are newer than the current state. For events that arrive late, either ignore them if superseded or queue them for reconciliation.
Run periodic jobs that query the gateway for the latest status of pending payments and compare with your internal state. If there's a mismatch, update your state and trigger any necessary downstream actions (e.g., pledge fulfillment or notification).
Log all webhook events, state transitions, and reconciliation outcomes. Set up alerts for high failure rates, stuck states, or reconciliation mismatches to quickly detect and resolve issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: I listed the obvious stuff (pledge created, payment attempted, payment succeeded, refund issued) and mentioned storing actor ID, timestamp, and a JSON blob for the diff.
Start by clarifying the purpose of the audit log—compliance, debugging, or analytics—then describe the table schema with key fields like event_id, timestamp, actor, action, and metadata. Walk through the donation lifecycle stages (initiation, payment, fulfillment, completion) and specify the events captured at each stage, emphasizing immutability and query patterns.
Pro tip: Mention that audit logs should be append-only and stored separately from transactional databases to avoid performance impact, and discuss how you'd handle schema evolution for new event types without breaking existing queries.
Ask whether the audit log is for compliance, debugging, or analytics, as this affects schema design and retention. Confirm the donation lifecycle stages to cover.
Describe core columns: event_id (UUID), timestamp, actor_id, actor_type, action, entity_type, entity_id, metadata (JSON), and ip_address. Explain why each is needed.
List key events per stage: donation initiated, payment authorized, payment captured, donation completed, refund issued, etc. For each, specify the action name and relevant metadata.
Explain append-only design, partitioning by date, indexing on entity_id and timestamp, and how to query for audit trails or analytics. Mention retention and archival policies.
Cover handling high write volume, ensuring immutability (e.g., write-once storage), and meeting regulatory requirements like GDPR or SOX. Mention encryption and access controls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.