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).
Technical Fluency and System Trade Offs
Covers foundational technical understanding needed to partner with engineering teams and to make informed trade off decisions. Topics include basic software architecture concepts application programming interfaces databases deployment pipelines testing strategies and the impact of technical debt. Also includes systems thinking such as how changes propagate through systems and trade offs like performance versus development time or scalability versus simplicity.
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.
Long Term Sustainability and Scalability of Solutions
Designing infrastructure that will remain maintainable and effective over 3-5 years. Considering technical debt, documentation, knowledge transfer, and how solutions will evolve. Discussion of reducing operational burden and building systems that scale gracefully as demands grow.
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.
Company Specific Technical Challenges
Prepares candidates to analyze and propose solutions for technical constraints and domain specific problems relevant to the hiring company. Interviewers will evaluate the ability to identify regulatory or compliance constraints, latency and throughput requirements, data consistency models, vendor or platform limitations, cost and operational trade offs, and to propose a research and validation plan plus an implementation approach tailored to the company context.
System Evolution and Technical Strategy
Approaches for evolving systems and planning long term technical direction. Topics include managing technical debt, planning incremental migrations or rewrites, roadmapping, versioning and backward compatibility, deprecation strategies, balancing short term product needs with long term architecture, and aligning technical strategy with business objectives. Good answers show a pragmatic plan for incremental change, governance, and measurable milestones.
High Availability and Disaster Recovery
Designing systems to remain available and recoverable in the face of infrastructure failures, outages, and disasters. Candidates should be able to define and reason about Recovery Time Objective and Recovery Point Objective targets and translate service level agreement goals such as 99.9 percent to 99.999 percent into architecture choices. Core topics include redundancy strategies such as N plus one and N plus two, active active and active passive deployment patterns, multi availability zone and multi region topologies, and the trade offs between same region high availability and cross region disaster recovery. Discuss load balancing and traffic shaping, redundant load balancer design, and algorithms such as round robin, least connections, and consistent hashing. Explain failover detection, health checks, automated versus manual failover, convergence and recovery timing, and orchestration of failover and reroute. Cover backup, snapshot, and restore strategies, replication and consistency trade offs for stateful components, leader election and split brain mitigation, runbooks and recovery playbooks, disaster recovery testing and drills, and cost and operational trade offs. Include capacity planning, autoscaling, network redundancy, and considerations for security and infrastructure hardening so that identity, key management, and logging remain available and recoverable. Emphasize monitoring, observability, alerting for availability signals, and validation through chaos engineering and regular failover exercises.
Scaling Fundamentals and Concepts
Core concepts required to reason about scaling decisions and to communicate clear approaches. Topics include the difference between vertical and horizontal scaling and their trade offs; stateless versus stateful service design and why statelessness enables horizontal scaling; basic load balancing and request distribution strategies; when and how to apply caching replication and partitioning; simple autoscaling concepts and common metrics used to trigger scaling; how to identify common bottlenecks and apply pragmatic mitigations; and fundamental trade offs between latency throughput cost and complexity. This topic tests conceptual clarity and the ability to map requirements to simple scaling approaches.
Trade-Off Analysis and Justification
Ability to identify key nonfunctional requirements and constraints and to compare alternative designs with clear, quantitative reasoning. Expect discussion of consistency versus availability, latency versus throughput, cost versus performance, operational complexity, and implementation risk. Candidates should demonstrate how to quantify trade offs using metrics such as latency percentiles, throughput, cost per request, and availability targets, how to choose appropriate consistency models and failure modes, and how to document and justify the selected architecture given product and business priorities.