Design & User Experience Topics
User experience design, frontend architecture, and design systems. Includes UX principles, accessibility, and design documentation.
Information Architecture and Content Design
Organizing product content and user interfaces for clarity and discoverability. Topics include information hierarchies, navigation and routing, user flows and journey mapping, wireframing and low fidelity exploration, content organization and labeling, progressive disclosure, dashboard layout and KPI placement, filters and drill downs, and ideation and sketching techniques. Evaluates the ability to align structure with user mental models and to iterate designs based on evidence.
Findings Presentation and Impact
Ability to clearly present analytical findings and insights to stakeholders, and explain how those findings shaped a decision, process, or outcome. Covers structuring a findings narrative (context, evidence, recommendation), choosing the right visualization or format for the data, tailoring depth and language for technical versus non-technical audiences, and demonstrating measurable impact and follow-through on recommendations.
Research Methodology Selection and Tradeoffs
Covers how to choose, justify, and execute research and analysis methods given research questions, stakeholder needs, and real world constraints such as limited time, budget, or access to users. Candidates should be able to compare qualitative methods such as interviews, usability testing, ethnography, and diary studies with quantitative methods such as surveys, analytics, split testing, and controlled experiments, and explain when and how to combine them into mixed methods designs. The topic includes core decision criteria and trade offs including generative versus evaluative goals, depth versus breadth, speed versus rigor, sample size and power considerations, cost versus validity, internal validity versus external generalizability, and short term versus longitudinal designs. Practical skills include aligning methodology to success metrics and business objectives, scoping minimal viable research designs, selecting sampling strategies and proxies, recruitment and instrumentation choices, pilot testing, estimation of sample size for quantitative work, mitigation of bias and threats to validity, documenting limitations and uncertainty, communicating and defending methodological choices to nonresearch stakeholders, and ensuring ethical and privacy safeguards and data quality in constrained or iterative studies.
Technical Depth & Areas of Specialization
Every strong candidate has one or more areas of technical depth that go beyond generalist knowledge. Discuss the area(s) where you have the most depth: how you identify it (a subsystem, technology, domain, or class of problem you gravitate toward), a concrete project or accomplishment that demonstrates that depth, how you actively keep that expertise current (reading, communities, side projects, postmortems), and how that depth changes the way you make trade-offs or collaborate with generalists on your team. Areas of specialization are highly individual and role-dependent (examples span distributed systems reliability, accessibility and design systems, security architecture, data pipelines, performance optimization, mobile platforms) - the interviewer should probe the candidate's own stated specialization rather than assume a fixed domain.
Interactive Reporting and User Experience
Design dashboards with appropriate interactivity: filters, drill-downs, tooltips, and bookmarks. Balance flexibility with simplicity; avoid analysis paralysis from too many filters. Understand how to guide users toward insights through progressive disclosure.
Usability Principles and Heuristics
Covers core usability principles and established heuristics used to evaluate and design user interfaces. Candidates should understand Nielsen style heuristics such as visibility of system status, match between system and the real world, user control and freedom, consistency and standards, error prevention and recovery, recognition rather than recall, flexibility and efficiency of use, aesthetic and minimalist design, help and documentation, and user freedom. Beyond listing heuristics, be prepared to explain how principles like feedback, affordance, discoverability, error prevention, progressive disclosure, accessibility, and reduction of cognitive load influence interaction design decisions. Expect to discuss methods for applying heuristics in practice, for example conducting heuristic evaluations, creating checklists, running usability tests, analyzing metrics such as task success rate, time on task, error rate, and System Usability Scale scores, and iterating designs based on findings. Interviewers may ask for concrete examples of trade offs you made, defects you detected with heuristics, how you prioritized fixes, and how you communicated usability issues to engineers and stakeholders.
Research Artifacts and Documentation
Skills for creating and managing research artifacts that communicate findings and support decision making, across any research-driven role (UX/design research, data science, market research, academic or applied research). Covers common artifact types: formal research reports, executive summaries, slide presentations, research briefs, personas and journey maps, analysis memos and write-ups, dashboards, and data visualizations. Emphasis on selecting the right artifact for the audience and purpose, balancing comprehensiveness with usability, ensuring clarity and reproducibility of findings, maintaining artifact quality and currency over time, applying templates and version control, and collaborating with stakeholders to disseminate insights effectively.
Learning from Feedback and Iteration
Evaluate how the candidate solicits, interprets, and incorporates feedback from users, teammates, and stakeholders to improve a product, design, or process. Areas include examples of iterative cycles driven by user testing or stakeholder input, specific pivots informed by feedback, changes to documentation or deliverables based on review, techniques for gathering and prioritizing feedback, and evidence of continuous improvement and valuing diverse perspectives.
Progressive Disclosure and Audience Design
Covers the principles and practices of structuring information for multiple audiences by using progressive disclosure to manage cognitive load. Candidates should be able to explain the theory of progressive disclosure, why limiting initial information reduces cognitive load, and when to reveal additional details on demand. This includes concrete strategies for serving beginners and advanced users or different roles and use cases within the same product documentation or interface: layered content (overview then details), summaries with expandable details, quick start guides, step by step tutorials, reference sections, role specific landing pages, contextual help, tooltips, and example driven content. Discuss design tradeoffs such as discoverability versus simplicity, maintaining consistency, versioning and referenceability, and accessibility considerations. Describe how to identify audience needs through personas, user research, and analytics, and how to structure navigation and information architecture so users can find the level of detail they need. Be prepared to give examples of implementation patterns, explain when progressive disclosure is inappropriate, and describe metrics to evaluate success such as task completion, time on task, support volume, heatmaps, and user feedback.