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).
Driver and Rider Experience Technology
Covers domain specific technologies that support transportation platform user experiences for drivers and riders. Topics include dispatch and matching systems, routing and navigation, mapping services, telematics and location services, payments and billing, in app communications, safety and reporting systems, latency and availability considerations for mobile platforms, and metrics and instrumentation used to measure and improve user experience.
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.
Scalability and Performance
Focuses on capacity planning, performance tradeoffs, and strategies for handling growth. Topics include the relationship between latency, throughput, consistency, and availability, when to accept eventual consistency, vertical versus horizontal scaling, caching, sharding, load distribution, back pressure and throttling patterns, performance testing and benchmarking, capacity forecasting, and triggers for scaling decisions. Candidates should be able to identify bottlenecks, justify tradeoffs between cost and performance, and recommend mitigation approaches for common performance problems.
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.
Making Difficult Technical Decisions
Situations where you had to make trade-offs, navigate competing priorities, or choose between technical approaches with real consequences.
System Design and Scalability
Covers architectural thinking and design tradeoffs for building reliable, high performance systems. Topics include design decision reasoning given constraints such as cost, latency and availability; scaling strategies including horizontal and vertical scaling, load balancing, caching patterns, database partitioning and sharding, read replicas, and asynchronous processing; capacity planning and observability; spotting and explaining bottlenecks such as hot partitions, single points of failure, database locks and network limits; and communicating technical impact in business terms. Candidates should be able to justify choices, compare alternatives, and articulate metrics and monitoring approaches to validate design decisions.
Scaling Systems and Platforms Through Growth
Describe experiences scaling systems, platforms, or services through significant growth phases. Examples: scaling from 1 million to 100 million users, migrating from monolith to microservices as organization grew, or building infrastructure to support 10x team growth. For each example: What was working before that stopped working at scale? What bottlenecks did you encounter? How did you identify and address them? What architectural changes were necessary? How did you sequence the work to minimize disruption? What did you learn? Discuss both technical and organizational scaling—they're intertwined.
Scalable System Design and Architecture
Focuses on designing systems that scale to large numbers of users and high load while balancing consistency, availability, and operational concerns. Candidates should discuss application programming interface design, data flow, load distribution and load balancing, caching strategies, database selection and partitioning, indexing, replication, consistency models, microservices boundaries, synchronous and asynchronous communication patterns, message and event architectures, deployment and rollout strategies, and measures for capacity planning and performance optimization. Answers should include trade off analysis, expected bottlenecks, quantification where possible, and rationale for architecture decisions.
System Design and Reliability
Design principles and trade offs for building highly scalable and reliable distributed systems. Expect discussion of capacity planning, partitioning and sharding, caching and load balancing strategies, replication and consistency models, latency and throughput trade offs, fault tolerance, graceful degradation, redundancy, disaster recovery, monitoring and alerting, and postmortem culture. Candidates should reason about non functional requirements and propose architectures meeting targets for scale, performance, and operational resilience.