I started with the happy path and the interviewer immediately asked about cross-currency transfers, which I hadn't fully thought through.
Start by clarifying requirements and scale, then design a layered architecture: API gateway, service layer, and data layer. Focus on the atomic transfer flow, especially cross-currency, by using distributed transactions or sagas with idempotency and compensation. Conclude with trade-offs and scalability considerations.
Pro tip: Emphasize idempotency and exactly-once semantics for transfers, as financial systems demand reliability; mention how you'd handle failures and retries without double-charging.
Ask about expected throughput, consistency requirements, supported currencies, and regulatory constraints. This shapes your design choices.
Define RESTful endpoints for account creation, balance inquiry, deposit, withdrawal, and transfer. Include idempotency keys and proper HTTP methods/status codes.
Design tables for accounts, transactions, and currency exchange rates. Consider using a ledger-based model for auditability and consistency.
Explain how a transfer works: validate accounts, lock balances, convert currency if needed, debit/credit atomically, and record the transaction. Use distributed transactions or sagas for cross-service atomicity.
Discuss partitioning, replication, caching, and failure handling. Mention monitoring, alerting, and reconciliation processes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope: single-node vs distributed, expected scale, and consistency requirements. Then walk through a layered design: ACID via transactions with appropriate isolation levels, idempotency via unique request IDs and deduplication, concurrency control via locking or optimistic versioning, and overdraft prevention via balance checks within the transaction. Finally, discuss trade-offs and failure modes.
Pro tip: Emphasize that idempotency and concurrency control must be enforced at the database level, not just in application code, and mention how you'd handle partial failures with retries and compensating actions.
Ask about scale, consistency needs, and whether the system is single-node or distributed. This determines whether you can rely on a single ACID database or need distributed transactions.
Use database transactions with appropriate isolation levels (e.g., Serializable or Repeatable Read) to atomically debit and credit accounts. For distributed systems, consider two-phase commit or Saga patterns with compensating transactions.
Assign a unique idempotency key to each transfer request. Store processed keys in a deduplication table with the result, so retries return the same response without re-executing the transfer.
Use pessimistic locking (SELECT FOR UPDATE) or optimistic concurrency control (version numbers) to serialize balance updates. Check the balance within the transaction before debiting to prevent overdrafts.
Compare locking vs optimistic approaches, and explain how you'd handle deadlocks, timeouts, and network partitions. Mention monitoring and alerting for failed transfers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about write-ahead logs and append-only ledger tables for auditability.
Start by clarifying the system's requirements and constraints, then walk through how you'd design each service to guarantee durability (e.g., write-ahead logging, replication), auditability (e.g., immutable logs, event sourcing), and consistency (e.g., distributed transactions, idempotency). Finally, explain your reconciliation strategy, such as periodic checks, compensating transactions, and automated repair, emphasizing trade-offs and monitoring.
Pro tip: Show that you think about failure modes and recovery from the start—mention how you'd design for idempotency and use dead-letter queues to handle inconsistencies, rather than treating reconciliation as an afterthought.
Ask about the system's scale, consistency needs (strong vs. eventual), latency tolerance, and regulatory audit requirements to tailor your approach.
Explain techniques like write-ahead logging, synchronous replication, and durable message queues to ensure data isn't lost even during failures.
Describe how you'd maintain immutable, tamper-evident logs (e.g., append-only ledgers, cryptographic hashing) and trace every change to its source.
Discuss patterns like sagas, two-phase commit, or idempotent consumers, and how you'd handle conflicts and partial failures.
Outline a reconciliation process: periodic audits, comparing state across services, detecting discrepancies, and automatically or manually repairing them with compensating actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sagas vs distributed transactions is one of those topics where I know the theory but explaining the tradeoffs out loud is harder than I expected.
Start by clarifying the system's requirements and constraints, then propose a sharding strategy based on access patterns and data model. For failures, compare sagas and distributed transactions, highlighting trade-offs in consistency, availability, and complexity, and recommend an approach aligned with business needs.
Pro tip: Demonstrate maturity by acknowledging that the choice between sagas and distributed transactions often depends on business requirements for consistency and the team's operational readiness, rather than purely technical merits.
Ask about scale, data volume, read/write patterns, consistency needs, and latency requirements to ground your design in reality.
Choose a shard key that distributes load evenly and minimizes cross-shard queries; discuss rebalancing and hotspot mitigation.
Explain that distributed transactions (e.g., 2PC) provide ACID but hurt availability and scalability, while sagas offer eventual consistency with compensating actions.
Based on requirements, recommend either sagas or distributed transactions, and describe how to handle failures (e.g., retries, idempotency, compensation).
Discuss monitoring, debugging, and testing strategies for the chosen approach, and how to evolve the system over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
PCI came up and I was upfront that I know the surface-level stuff but haven't worked in a PCI-scoped environment directly.
Structure your answer by walking through the security layers of the system in a logical order: authentication, authorization, encryption, rate limiting, and PCI compliance. For each layer, explain the specific mechanisms you would use, the trade-offs involved, and how they integrate to protect the system and meet regulatory requirements.
Pro tip: Emphasize that security is not a one-time implementation but an ongoing process; mention the importance of regular security audits, penetration testing, and staying updated with the latest vulnerabilities and patches.
Describe how users and services are authenticated, such as using OAuth 2.0, OpenID Connect, or multi-factor authentication. Discuss trade-offs between security and user experience.
Explain how access control is enforced, e.g., role-based access control (RBAC) or attribute-based access control (ABAC). Highlight the principle of least privilege and how to handle permissions at scale.
Cover encryption in transit (TLS) and at rest (AES-256). Discuss key management, certificate rotation, and how encryption protects data integrity and confidentiality.
Explain how rate limiting prevents abuse and DDoS attacks. Mention algorithms like token bucket or leaky bucket, and how to apply limits per user, IP, or API key.
If handling payment card data, outline PCI DSS requirements: secure network, encryption, access control, monitoring, and regular testing. Discuss how to minimize scope by tokenization or using third-party processors.
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 critical user journeys and SLOs, then structure your answer around the four golden signals (latency, traffic, errors, saturation) and how they map to user-facing metrics. Emphasize proactive detection through alerting on SLO burn rates and anomaly detection, and tie monitoring back to business impact.
Pro tip: Meta values a metrics-driven, user-first approach: always connect technical signals to user experience and business metrics, and mention how you'd use canary deployments and real-user monitoring to catch issues before they affect everyone.
Ask clarifying questions to understand the system's architecture, critical user journeys, and existing SLOs/SLIs. This ensures your monitoring plan is tailored and relevant.
Identify the most important signals to track, such as the four golden signals (latency, traffic, errors, saturation) and business metrics (e.g., conversion rate, DAU). Explain why each matters for early problem detection.
Describe how you would instrument the system (e.g., metrics, logs, traces) and configure alerts based on SLO burn rates, anomaly detection, and thresholds. Mention tools like Prometheus, Grafana, or Meta's internal tools.
Explain strategies to detect problems early, such as canary deployments, synthetic monitoring, real-user monitoring, and load testing. Highlight the importance of alerting on leading indicators.
Discuss how you would continuously refine monitoring by analyzing incidents, conducting post-mortems, and adjusting thresholds. Emphasize a feedback loop for reliability improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.