I started with the happy path and drew out the state machine for an application going from draft to submitted to approved.
Start by clarifying requirements and constraints, then design a stateful application workflow with clear states and transitions. Define RESTful APIs for each action (start, save, submit, resume) with idempotency and security in mind, and discuss data storage, concurrency, and integration with KYC/AML services.
Pro tip: Emphasize idempotency and resumability: use a unique application ID and versioning to handle concurrent saves and network retries, and store partial state securely. This shows you understand real-world fintech reliability needs.
Ask about expected scale, regulatory requirements (KYC/AML), supported regions, and whether the flow is for retail or institutional clients. Confirm non-functional needs like latency, availability, and security.
Model the application lifecycle (e.g., DRAFT, SUBMITTED, UNDER_REVIEW, APPROVED, REJECTED) and allowed transitions. This ensures the API design aligns with business logic and auditability.
Specify endpoints for starting (POST /applications), saving (PATCH /applications/{id}), submitting (POST /applications/{id}/submit), and resuming (GET /applications/{id}). Include request/response schemas, status codes, and error handling.
Choose a durable store (e.g., SQL with JSON columns or NoSQL) for partial applications. Implement optimistic locking or versioning to prevent lost updates during concurrent saves.
Incorporate authentication/authorization, encryption at rest/in transit, and idempotency keys. Discuss integration with KYC providers, fraud checks, and notification systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the workflow's goals and constraints, then propose a layered architecture where synchronous checks are minimal and asynchronous checks handle heavy lifting. Emphasize risk-based decision-making and user experience trade-offs.
Pro tip: Mention that KYC/AML checks should be idempotent and auditable, and that you'd use a state machine to track verification status across retries and failures.
Ask about the specific workflow (e.g., onboarding, transaction), regulatory requirements, latency SLAs, and user experience expectations.
Identify which checks are mandatory before proceeding (e.g., sanctions screening for high-risk users) versus those that can be deferred (e.g., document verification for low-risk users).
Make only fast, deterministic checks synchronous (e.g., basic format validation, internal blacklist lookup) and offload time-consuming or third-party checks to asynchronous queues with callbacks.
Use a state machine to track each check's status, handle retries, and ensure idempotency. Provide clear user feedback for pending states.
Define fallback behavior for timeouts or failures (e.g., manual review, temporary holds) and ensure all actions are logged for audit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about deduplication at the API layer using idempotency keys, plus a uniqueness constraint on PII fields like email and government ID hash at the database level.
Start by clarifying the definition of a duplicate account (e.g., same email, phone, government ID, or device fingerprint) and the business context at Coinbase (KYC/AML requirements). Then propose a layered prevention strategy: enforce uniqueness at the database level, add application-level checks, and incorporate identity verification and risk signals. Finally, discuss trade-offs, edge cases, and how to handle false positives.
Pro tip: Emphasize that preventing duplicates is not just a technical problem but also a business and compliance requirement; mention that you would collaborate with fraud, compliance, and product teams to define the right signals and thresholds.
Ask what constitutes a duplicate (email, phone, SSN, device ID, etc.) and what the business impact is (fraud, regulatory, user experience). This shows you understand the problem space before jumping to solutions.
Propose database constraints (unique indexes) on key identifiers like email, phone number, and government ID. Discuss how to handle soft deletes and case-insensitive comparisons.
Implement pre-insert checks and real-time validation during signup. Use normalized data and consider fuzzy matching for names/addresses to catch near-duplicates.
Integrate KYC providers, device fingerprinting, IP analysis, and behavioral signals to detect and block duplicate attempts. Use a risk score to decide whether to allow, challenge, or block.
Set up monitoring for duplicate creation attempts, false positives, and user friction. Define a process for manual review and account merging, and continuously refine rules based on data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with WebSockets for active sessions and a polling fallback, plus push notifications for users who closed the tab.
Start by clarifying the requirements: what types of status updates, expected latency, scale, and client platforms. Then propose a real-time push architecture using WebSockets or Server-Sent Events, with a fallback to polling, and discuss how you'd handle reliability, scalability, and security in a fintech context like Coinbase.
Pro tip: Emphasize idempotency and exactly-once delivery semantics for status updates, especially for financial transactions, and mention how you'd handle reconnection and missed events with a sequence number or event log.
Ask about the types of status updates (e.g., transaction confirmations, order fills), expected update frequency, latency tolerance, number of concurrent users, and client types (web, mobile).
Evaluate WebSockets, Server-Sent Events (SSE), and long polling. For Coinbase, WebSockets are ideal for bidirectional, low-latency updates, but SSE can work for one-way status streams.
Outline a pub/sub system (e.g., Redis Pub/Sub, Kafka) to fan out updates from services to a connection gateway that manages client sessions. Ensure horizontal scalability and fault tolerance.
Incorporate sequence numbers, acknowledgments, and idempotent updates to handle reconnections and missed messages. Discuss how to recover state after a disconnect.
Mention authentication (e.g., JWT), authorization, encryption (TLS), and rate limiting. For Coinbase, ensure compliance with financial regulations and audit logging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Described a pre-signed URL flow to upload directly to object storage, then a background job that calls a document verification vendor.
Start by clarifying the requirements and constraints, such as document types, volume, and compliance needs. Then outline a high-level architecture covering upload, storage, verification, and security, and dive into trade-offs and failure handling. Emphasize scalability, reliability, and user experience throughout.
Pro tip: Highlight how you would handle edge cases like blurry documents or fraudulent uploads, and discuss how you'd monitor and iterate on the system post-launch. This shows you think beyond the happy path and consider real-world operational challenges.
Ask about document types, expected volume, latency requirements, compliance (e.g., KYC/AML), and user experience expectations. This ensures your design meets the actual needs.
Outline the main components: client-side upload, secure transmission, storage (e.g., S3), verification service (OCR, third-party APIs), and result handling. Mention asynchronous processing for scalability.
Explain how you'd verify documents: extract data via OCR, validate against expected formats, check for tampering, and integrate with identity verification providers. Discuss handling failures and retries.
Describe encryption at rest and in transit, access controls, audit logging, and data retention policies. Mention compliance standards like GDPR, CCPA, and financial regulations.
Discuss trade-offs between synchronous vs asynchronous processing, cost vs performance, and build vs buy for verification. Explain how the system scales with increasing load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I split applicant (person) from application (process instance) early on, which felt right.
Start by outlining the core entities (Applicant, Application) and their relationships, then discuss how to model them for scalability and query patterns. Transition into PII security by covering encryption at rest and in transit, access control mechanisms, and compliance considerations relevant to Coinbase's regulatory environment.
Pro tip: Emphasize a data-centric security approach: encrypt PII at the field level and use tokenization to minimize exposure, while implementing least-privilege access with audit trails. This shows you understand both engineering and compliance needs in a fintech context.
Define the main entities: Applicant (personal details, contact info) and Application (submitted data, status, timestamps). Establish relationships (one applicant can have multiple applications) and consider normalization vs. denormalization based on access patterns.
Choose appropriate data stores (e.g., relational for transactional integrity, NoSQL for flexible schemas) and design tables/collections with indexes to support common queries like fetching applications by applicant or status. Consider partitioning and sharding for scale.
Identify which fields contain PII (e.g., SSN, address, email). Apply encryption at rest (AES-256) and in transit (TLS). Use field-level encryption or tokenization for sensitive data to limit exposure, and manage keys securely with a KMS.
Enforce least privilege with role-based access control (RBAC) or attribute-based access control (ABAC). Ensure all access to PII is logged and monitored, and implement data masking for non-production environments.
Align with regulations like GDPR, CCPA, and financial industry standards. Define data retention policies, secure deletion, and consent management. Consider data residency requirements for global operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Append-only event log, each state transition writes a record with who triggered it, when, and from what IP.
Start by clarifying the system's requirements and compliance needs, then design an append-only, tamper-evident audit log that captures who did what, when, and from where. Discuss storage, querying, retention, and how to integrate audit trails without impacting performance or security.
Pro tip: Emphasize that audit trails must be immutable and tamper-evident, and mention using cryptographic hashing or blockchain-like chaining to ensure integrity—this resonates well in a crypto company like Coinbase.
Ask about regulatory requirements (e.g., SOX, GDPR, PCI), what events need auditing, retention period, and who will access the audit logs.
Define the schema for audit events: timestamp, actor, action, resource, before/after values, IP, user agent, and a unique event ID. Ensure it's append-only.
Select a durable, scalable storage (e.g., write-once object store, append-only database) and implement tamper-evidence via cryptographic hashing or chaining.
Integrate audit logging into the application, possibly using an event-driven approach (e.g., publish events to a queue) to decouple and ensure reliability.
Provide efficient querying for auditors, enforce strict access controls, and automate retention policies (e.g., archival or deletion after X years).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Exponential backoff with jitter, dead-letter queues for things that keep failing, and a manual review queue as the final fallback.
Start by acknowledging that failures are inevitable and must be handled gracefully. Then, outline a layered strategy: classify failures, implement retries with backoff and jitter, use circuit breakers, and ensure idempotency. Finally, discuss trade-offs and monitoring, emphasizing user experience and data consistency.
Pro tip: Emphasize idempotency keys for external calls like KYC to prevent duplicate submissions, and mention that you'd align retry policies with the provider's rate limits and SLAs to avoid penalties.
Distinguish between transient (network blips, timeouts) and permanent (invalid input, auth errors) failures. Only retry transient errors.
Use exponential backoff with jitter, set a max retry limit, and consider the overall timeout budget. For critical flows, use a dead-letter queue for later analysis.
Prevent cascading failures by tripping the circuit after repeated failures, and fall back to a degraded mode or queue for later processing.
Use idempotency keys for external calls to avoid duplicate side effects when retrying. For KYC, ensure that repeated submissions don't create multiple cases.
Track retry rates, failure types, and circuit breaker states. Set up alerts for anomalies and use logs to debug issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rate limiting I covered at the API gateway level, keyed on IP and user identity separately.
Start by clarifying the workflow's critical paths and failure modes, then layer rate limiting (e.g., token bucket per user/IP) and risk scoring (e.g., rules + ML) as independent but coordinated services. Emphasize trade-offs between latency, accuracy, and availability, and how you'd handle false positives/negatives in a financial context.
Pro tip: At Coinbase, fraud prevention is a business-critical function, so show you understand that rate limiting and risk scoring must be tunable without deploys and must degrade gracefully—e.g., fall back to conservative rules if the ML service is down.
Ask about expected traffic volume, latency SLAs, regulatory requirements, and what constitutes fraud vs. legitimate behavior. Identify the workflow's entry points and sensitive operations.
Choose an algorithm (token bucket, sliding window) and enforcement point (API gateway, service mesh, or application). Discuss distributed rate limiting with Redis or a dedicated service, and how to handle bursts and global vs. per-user limits.
Outline a pipeline: collect signals (device, IP, velocity, historical behavior), compute a score via rules and/or ML model, and define actions (allow, challenge, block). Explain how to keep scoring fast and how to update models without downtime.
Show how rate limiting and risk scoring work together—e.g., rate limiting as a first line of defense, then risk scoring for finer-grained decisions. Discuss ordering, fallbacks, and how to avoid double-counting or conflicting actions.
Cover monitoring, alerting, A/B testing, and feedback loops for false positives/negatives. Explain how to handle failures (e.g., circuit breakers) and ensure auditability for compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a multi-region active-active setup with data residency constraints for regulated markets, async replication for non-critical data, and strong consistency only where legally required like the dedup check.
Start by clarifying the scale and requirements, then propose a high-level architecture that partitions the system by geographic region and uses horizontal scaling. Focus on trade-offs between consistency, latency, and cost, and explain how you would handle data replication, load balancing, and failure recovery.
Pro tip: Emphasize the importance of monitoring and observability from day one, and discuss how you would use metrics to drive capacity planning and auto-scaling decisions. Also, mention regulatory considerations like data residency, which is crucial for a global financial platform like Coinbase.
Ask questions to understand the expected read/write ratio, latency requirements, consistency needs, and budget constraints. Confirm the definition of 'application' in this context (e.g., user sign-ups, transactions).
Propose deploying the system in multiple regions with data partitioned by user geography. Use a global load balancer to route users to the nearest region, and consider active-active or active-passive setups for disaster recovery.
Break down the system into microservices and scale each independently. Use stateless services, sharding for databases, and caching to handle high read volumes. Consider asynchronous processing for non-critical tasks.
Discuss trade-offs between strong and eventual consistency. For financial data, strong consistency may be required within a region, while cross-region replication can be eventual. Mention conflict resolution strategies if needed.
Describe how you would monitor key metrics (latency, error rates, throughput) and set up auto-scaling policies. Outline a disaster recovery plan with regular backups and failover testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.