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).
Real-Time Ride Matching and Proximity Algorithms
Techniques for building real-time, large-scale ride-matching systems in distributed architectures, including geo-aware proximity algorithms, spatial indexing, latency optimization, scheduling between drivers and riders, fault tolerance, and microservices-based design patterns.
Resilience and Chaos Engineering
Covers identifying system failure modes and designing resilient distributed systems, plus proactive resilience testing through controlled failure injection. Topics include common failure modes such as network partitions, increased latency, resource exhaustion, cascading failures, and data corruption; resilience design patterns like graceful degradation, retries with backoff, circuit breakers, bulkheads, timeouts, rate limiting, redundancy, and replication; and operational practices such as monitoring, distributed tracing, metrics and alerting to detect and diagnose failures. Also includes chaos engineering methodologies: defining steady state and hypotheses, designing safe experiments, controlling blast radius, tooling and frameworks, running game days, producing recovery runbooks and playbooks, handling test induced outages versus real incidents, and feeding lessons learned into postmortems and system improvements. Emphasis is on designing experiments that validate assumptions without causing uncontrolled production outages and on translating chaos results into concrete reliability improvements.
Decision Making Under Uncertainty
Focuses on frameworks, heuristics, and judgment used to make timely, defensible choices when information is incomplete, conflicting, or evolving. Topics include diagnosing unknowns, defining decision criteria, weighing probabilities and impacts, expected value and cost benefit thinking, setting contingency and rollback triggers, risk tolerance and mitigation, and communicating uncertainty to stakeholders. This area also covers when to prototype or run experiments versus making an operational decision, how to escalate appropriately, trade off analysis under time pressure, and the ways senior candidates incorporate strategic considerations and organizational constraints into choices.