Systems Architecture & Distributed Systems Topics
Large-scale distributed system design, service architecture, microservices patterns, global distribution strategies, scalability, and fault tolerance at the service/application layer. Covers microservices decomposition, caching strategies, API design, eventual consistency, multi-region systems, and architectural resilience patterns. Excludes storage and database optimization (see Database Engineering & Data Systems), data pipeline infrastructure (see Data Engineering & Analytics Infrastructure), and infrastructure platform design (see Cloud & Infrastructure).
Fault Tolerance and Failure Scenarios
Designing systems resilient to component failures: timeouts, retries with exponential backoff, circuit breakers, bulkheads. Discuss cascading failure prevention and graceful degradation. At Staff level, demonstrate thinking about multi-layer failures (service failures, database failures, network partitions) and how to detect and recover from them.
Systems Design and Scalability
Focuses on designing scalable distributed systems and marketplace architectures. Topics include core marketplace components such as search and discovery, real time availability, booking and reservation flows, payment processing, and host to guest matching and how those systems interact. Expect to identify scalability bottlenecks, propose caching strategies, database optimization including sharding and replication, horizontal scaling approaches, and reason about consistency versus availability trade offs. Also cover real time synchronization strategies, handling race conditions such as double booking, event driven designs and message based architectures, and considerations for monitoring and operational resilience.
Data Consistency and Reliability
Assesses understanding of data consistency models and techniques for building reliable systems. Topics include the CAP theorem and practical implications, strong versus eventual consistency, transactional guarantees and isolation levels, distributed consensus and quorum protocols, replication strategies, idempotency and safe retry semantics, failure modes and recovery strategies, and monitoring and testing approaches that surface consistency issues. Candidates should explain trade offs among correctness, availability, and latency and describe patterns used to maintain data integrity under real world failures.
CAP Theorem and Consistency Models
Understand the CAP theorem and how Consistency, Availability, and Partition Tolerance interact in distributed systems. Know different consistency models including strong consistency such as linearizability, eventual consistency, causal consistency, and session consistency, and how to apply them to different use cases. Be familiar with consensus protocols and distributed coordination primitives such as Raft and Paxos, quorum reads and writes, two phase commit and when to use them. Understand trade offs between consistency and availability under network partitions, patterns for hybrid approaches where different data uses different guarantees, and the product and developer experience implications such as latency, stale reads, and API contract clarity.
System Design and Architecture
Design large scale reliable systems that meet requirements for scale latency cost and durability. Cover distributed patterns such as publisher subscriber models caching sharding load balancing replication strategies and fault tolerance, trade off analysis among consistency availability and partition tolerance, and selection of storage technologies including relational and nonrelational databases with reasoning about replication and consistency guarantees.
Migration and Modernization Strategy
Covers planning and executing large scale technology transformations such as migrating a monolithic application to microservices, replatforming from on premises to cloud, major framework or database upgrades, and full platform rearchitectures. Includes selection and justification of migration approaches and patterns for different business goals, for example strangler fig, forklift or lift and shift, incremental refactor, big bang replacement, parallel run, and coexistence strategies. Describes phasing and rollout planning to maintain product velocity, sequencing work to maximize business value, and staging and rollback plans to reduce operational and business risk. Addresses data migration planning, validation, consistency and synchronization approaches, testing and verification strategies to minimize downtime and customer impact, and fallback and rollback mechanisms. Covers engineering practices such as deployment automation, continuous integration and continuous delivery, observability and monitoring, and performance and capacity planning. Also includes architectural techniques such as application programming interface wrapping and adapter patterns to enable interoperability between legacy and new systems, governance and compliance considerations, security during migration, cross functional stakeholder communication and coordination, and how to define and measure success through key performance indicators and post migration validation.
Data Consistency and Distributed Transactions
In depth focus on data consistency models and practical approaches to maintaining correctness across distributed components. Covers strong consistency models including linearizability and serializability, causal consistency, eventual consistency, and the implications of each for replication, latency, and user experience. Discusses CAP theorem implications for consistency choices, idempotency, exactly once and at least once semantics, concurrency control and isolation levels, handling race conditions and conflict resolution, and concrete patterns for coordinating updates across services such as two phase commit, three phase commit, and the saga pattern with compensating transactions. Also includes operational challenges like retries, timeouts, ordering, clocks and monotonic timestamps, trade offs between throughput and consistency, and when eventual consistency is acceptable versus when strong consistency is required for correctness (for example financial systems versus social feeds).
Trade Off Analysis and Decision Frameworks
Covers the practice of structured trade off evaluation and repeatable decision processes across product and technical domains. Topics include enumerating alternatives, defining evaluation criteria such as cost risk time to market and user impact, building scoring matrices and weighted models, running sensitivity or scenario analysis, documenting assumptions, surfacing constraints, and communicating clear recommendations with mitigation plans. Interviewers will assess the candidate's ability to justify choices logically, quantify impacts when possible, and explain governance or escalation mechanisms used to make consistent decisions.
Architecture and Technical Trade Offs
Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.