Start by clarifying functional and non-functional requirements, then outline a high-level architecture covering the main components: user onboarding, market data, order management, and portfolio services. Dive into critical flows like order placement with idempotency and risk checks, and discuss data models and trade-offs for scalability and consistency.
Pro tip: Emphasize idempotency and exactly-once processing in order placement and clearing, as these are crucial for financial systems to avoid duplicate trades and ensure data integrity. Also, discuss how you'd handle market data bursts and ensure low-latency streaming.
Ask about expected user scale, latency requirements, regulatory constraints, and supported asset types. Define functional requirements like real-time quotes, order types, and portfolio tracking.
Sketch the main services: API gateway, user service, market data service, order service, portfolio service, and clearing service. Choose appropriate data stores (e.g., time-series DB for quotes, relational for orders) and messaging for async communication.
Detail the order lifecycle: validation, risk checks, idempotency (using client-generated order IDs), routing to exchanges/market makers, and post-trade clearing. Explain how to maintain real-time balances and positions with cost basis and P&L.
Discuss partitioning, replication, and failover for high availability. Cover security (encryption, auth), regulatory reporting, and how to handle corporate actions (splits, dividends) via event-driven updates.
Highlight key trade-offs (e.g., consistency vs. availability, latency vs. cost) and suggest potential enhancements like ML for risk or blockchain for settlement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with separate stores: an append-only event log for orders and trades, a relational DB for account and position state, and a time-series store for ticks.
Start by clarifying the domain and scale (e.g., Fanatics' e-commerce vs. trading systems), then propose a polyglot persistence strategy that matches each data type to an appropriate store. Explicitly discuss consistency and latency tradeoffs, especially between order state (strong consistency) and portfolio views (eventual consistency), and tie your choices to business requirements.
Pro tip: Frame the tradeoff as a business decision: strong consistency for orders prevents financial loss, while eventual consistency for portfolio views enables horizontal scale and low-latency reads. Mention that you'd validate with concrete SLAs and load estimates before committing.
Ask about expected throughput, data volume, latency SLAs, and consistency requirements for each data type. This shows you don't jump to solutions without understanding the problem.
Match each data type to a store: orders in a relational/ACID database, trades in an append-only log or time-series DB, account state in a strongly consistent KV store, and market ticks in a time-series or columnar store.
Contrast strong consistency for order state (to avoid overselling or double-spending) with eventual consistency for portfolio views (to allow fast, scalable reads). Discuss how CQRS and event sourcing can bridge the two.
Describe how events propagate from the order system to update portfolio views asynchronously, and how you'd handle failures, retries, and idempotency.
Conclude by reiterating that the choices are driven by business needs: correctness for orders, availability and speed for views, and cost/operational complexity as a secondary factor.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with something like: an order service, a market data service, a portfolio service, a risk service, and a notification service.
Start by clarifying the system's domain and requirements, then propose a decomposition based on business capabilities and bounded contexts. For each service, justify the choice of synchronous APIs for request-response interactions versus asynchronous messaging for event-driven workflows, and explain how message queues and event sourcing support scalability and resilience.
Pro tip: Tie your decisions to concrete trade-offs like latency, consistency, and failure modes, and mention how you'd evolve the architecture over time—interviewers value pragmatic evolution over dogmatic patterns.
Ask questions to understand the system's core functionality, scale, consistency needs, and team structure. Identify key business capabilities and data ownership boundaries.
Propose services aligned with bounded contexts (e.g., user, catalog, orders, payments). Explain how each service owns its data and exposes well-defined interfaces.
For each service interaction, decide between synchronous APIs (e.g., REST/gRPC) for immediate responses and asynchronous messaging for decoupling, buffering, and event propagation. Justify with trade-offs.
Describe the use of message queues (e.g., Kafka, RabbitMQ) for reliable delivery and event sourcing for auditability and temporal queries. Explain how events drive state changes and enable eventual consistency.
Discuss challenges like consistency, debugging, and operational complexity. Suggest how to evolve the architecture incrementally, e.g., starting with a monolith and extracting services as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Market open is the classic thundering herd problem for trading systems.
Start by clarifying the scale and reliability requirements for market open surges, then outline a resilient architecture that handles high throughput and low latency. Discuss specific failure modes like thundering herd, data staleness, and partial failures, and explain mitigation strategies such as circuit breakers, fallbacks, and graceful degradation. Finally, detail a market data outage plan with redundancy, failover, and recovery procedures.
Pro tip: Emphasize the importance of monitoring and observability to detect anomalies early, and share a real-world example of how you handled a similar surge or outage. This shows practical experience and a proactive mindset.
Ask about expected peak load, latency SLAs, data consistency needs, and regulatory requirements to tailor your design.
Propose a horizontally scalable, fault-tolerant architecture using techniques like sharding, caching, async processing, and load shedding.
Discuss failure modes such as thundering herd, cascading failures, data corruption, and network partitions, and how to mitigate them.
Outline a strategy with redundant data feeds, automatic failover, stale data handling, and circuit breakers to prevent system-wide impact.
Describe monitoring, alerting, and post-mortem processes to learn from incidents and improve resilience over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining a layered observability strategy covering metrics, logs, and traces, then explain how you'd design compliance logging and auditing to meet regulatory and security needs. Finally, walk through a structured incident response process, emphasizing detection, mitigation, root cause analysis, and post-mortem improvements.
Pro tip: Tie your observability and incident response choices directly to business impact—like protecting revenue during peak traffic or ensuring customer trust—to show you think beyond just technical metrics.
Describe how you'd instrument the system with metrics (e.g., latency, error rates), structured logs, and distributed tracing to gain full visibility into system health and user experience.
Explain how you'd capture immutable audit trails for sensitive actions, ensure log integrity and retention, and align with standards like GDPR, PCI-DSS, or SOX as relevant to Fanatics' e-commerce and sports betting domains.
Outline a clear on-call and escalation path, with defined severity levels, communication protocols, and runbooks to quickly detect, triage, and mitigate production failures.
After resolving the incident, lead a blameless post-mortem to identify root causes, document lessons learned, and create action items to prevent recurrence.
Close the loop by feeding insights back into observability and response plans, and regularly test incident readiness through game days or chaos engineering.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Saved the least prep time for this one and it showed.
Start by identifying the core abstractions in your current design that can be generalized, then outline how to extend them for new asset classes like options and crypto. Emphasize modularity, data model flexibility, and incremental delivery to manage complexity and risk.
Pro tip: Show awareness of domain-specific challenges (e.g., options pricing models, crypto volatility and 24/7 trading) and how they impact system design decisions like latency, consistency, and compliance.
Review the current design to pinpoint components that are asset-agnostic, such as order management, matching engine, and portfolio tracking. Determine which parts need generalization versus specialization.
Propose flexible schemas that can represent diverse asset types, including options (with strikes, expiries, Greeks) and cryptocurrencies (with wallets, blockchain specifics). Consider using inheritance or composition in your domain model.
Outline how to incorporate new rules for options (e.g., exercise, assignment) and crypto (e.g., 24/7 trading, no settlement delay). Discuss pluggable strategy patterns or rule engines to isolate asset-specific logic.
Explain how the system would handle increased load and different performance requirements, such as low-latency for options pricing or high throughput for crypto transactions. Mention horizontal scaling, caching, and async processing.
Describe a phased approach to add support, starting with a minimal viable feature set and iterating based on feedback. Highlight the importance of feature flags, monitoring, and rollback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.