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).
Workflow and Campaign Architecture for Scale
How to design marketing automation workflows that can handle scale (millions of contacts, complex branching logic). How to manage workflow complexity to avoid spaghetti logic. Reusable workflow components vs. one-off campaigns. How to ensure workflows perform efficiently. Version control and governance for workflows. How to handoff workflows from technical team to marketing team for ongoing management.
Technical Innovation and Modernization
Covers leading and executing technical change that raises the engineering bar while preserving operational stability. Topics include identifying and prioritizing innovation opportunities, sponsoring research and experimentation, running proofs of concept and pilots, and introducing new tools or frameworks. Also includes strategies for modernizing legacy systems and architecture with minimal business disruption, managing technical debt, migration planning, rollback and cutover approaches, and maintaining reliability and continuity. Evaluated skills include optimizing performance and cost at scale, establishing engineering standards and best practices, governance and risk management, stakeholder alignment and communication, measuring impact and return on investment, and balancing long term innovation with short term pragmatism.
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.
Architecture Trade Offs and Cost Analysis
Covers making and communicating architectural decisions that balance trade offs across cost, performance, reliability, speed to market, and organizational complexity. Topics include comparing architectural approaches and tool selections, estimating and explaining costs such as licensing, implementation, maintenance, compute, storage, and data transfer, and understanding how costs scale. Includes business driven framing of technical decisions, cloud economics including capital expenditure versus operational expenditure, return on investment analysis, and Total Cost of Ownership considerations. Candidates should be able to perform rough cost estimation, describe cost optimization strategies including rightsizing and managed service trade offs, and explicitly articulate constraints and choices when prioritizing features, timelines, and resources.
Technical Decision Making and Trade Offs
How to evaluate and clearly articulate trade offs when choosing technologies and designing solutions. This includes weighing reliability, performance, cost, development time, and operational complexity; comparing alternatives; identifying risks and mitigation plans; and explaining why a particular approach best meets current constraints and future needs. Strong answers show a metrics oriented mindset, consideration of team capabilities, and a willingness to revise decisions as new data arrives.
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.