Amazon behavioural interview template for Product Designer (Seattle, AWS/Consumer)

Purpose: Assess how a Product Designer demonstrates Amazon’s Leadership Principles (LPs) through past behaviour, with emphasis on customer-obsessed, data-informed, end-to-end design execution in ambiguous, high-scale contexts. What this interview covers at Amazon: - Core LPs probed for this role: Customer Obsession, Dive Deep, Invent and Simplify, Ownership, Bias for Action, Insist on the Highest Standards, Earn Trust, Have Backbone; Disagree and Commit, Deliver Results, Think Big, Learn and Be Curious, Frugality, Strive to be Earth’s Best Employer, Success and Scale Bring Broad Responsibility. - Product-design specific focus: problem framing, research rigor, design craft and systems thinking, accessibility/inclusive design, partnering with PM/Engineering/Science, experimentation (A/B, UX metrics), and measurable business/customer outcomes. - Amazon style: STAR(+L) answers (Situation, Task, Actions, Results, Learnings), rigorous follow-ups, metric depth checks, and a Bar Raiser-caliber evaluation of long-term behaviours and decision quality. Structure (60 minutes): - 0–5 min: Warm-up and expectations. Outline LP focus and STAR(+L) format. - 5–35 min: LP deep dives (4–5 stories). Each is probed with factual, metric, and trade-off follow-ups. - 35–50 min: Cross-functional/ambiguity scenarios tailored to Amazon scale (consumer, marketplace, or AWS console contexts). - 50–55 min: Candidate questions (signals: Learn and Be Curious, Customer Obsession). - 55–60 min: Wrap-up and next steps. Question bank (behavioural, product-design specific): - Customer Obsession: Tell me about a time you discovered a critical customer pain through research and changed the roadmap. What evidence convinced the team? What metric moved? - Dive Deep: Describe a design issue you debugged using logs, experiment data, or cohort analysis. What surprised you in the data? How did it alter the design? - Invent and Simplify: Share a time you simplified a complex workflow (e.g., seller onboarding, checkout, AWS console task). What did you remove, and how did you protect edge cases? - Ownership: Tell me about a v1 you shipped where requirements were unclear. How did you de-risk, sequence milestones, and ensure launch quality? - Bias for Action: Describe a time you shipped a scrappy iteration to learn. What guardrails and success metrics did you set? How quickly did you iterate? - Insist on the Highest Standards: Example of raising the bar on accessibility or design quality under a tight deadline. What trade-offs did you reject and why? - Earn Trust: Tell me about a conflict with a PM or engineer on scope/tech feasibility. How did you influence without authority? - Have Backbone; Disagree and Commit: When did you disagree with leadership on a customer problem or design direction? What was the data, and how did you commit afterward? - Deliver Results: Example where you hit a hard date (e.g., Prime Day, re:Invent) with measurable impact. How did you track and unblock? - Think Big: Describe a north-star vision you created. How did you ladder it into a pragmatic roadmap and design system components? - Learn and Be Curious: How have you upleveled your craft (e.g., motion, service design, gen-AI tooling) and applied it to customer problems? - Frugality: When did constraints lead to a better design solution? What did you intentionally not build? - Earth’s Best Employer / Broad Responsibility: Example of designing for inclusion, safety, or societal impact (e.g., privacy, fraud, misinformation). What long-term risks did you anticipate? Deep-dive follow-ups (used throughout): - Metrics: What was the baseline, target, and final impact (e.g., task success, CSAT, defect rate, conversion, latency, engagement, support tickets)? - Decision quality: What alternatives did you evaluate? Why did you reject them? What would you do differently now? - Mechanisms: What recurring mechanism (ritual, checklist, dashboard, design token, experiment template) did you create to prevent regressions? Evaluation rubric (signals): - Strong hire: Repeated, principled customer-first decisions; crisp STAR structure; quantifiable outcomes; comfort with data/experiments; raises quality bar; credible trade-off narratives; constructive conflict and alignment; mechanisms that scale; reflection with learnings. - Mixed: Vague problems/metrics; process over outcomes; limited influence; shallow root-cause analysis; ad-hoc craft without systems thinking. - No hire: Lacks customer evidence; hand-wavy results; blames others; poor ownership; unsafe or inaccessible designs; cannot Dive Deep into metrics or decisions. Candidate guidance (shared upfront): - Bring 5–6 STAR stories spanning different products, customers, and failure/learning moments. Include at least one accessibility and one data-informed iteration example. - Quantify impact where possible; be ready to discuss metrics sources (experiments, telemetry, surveys) and design artifacts (flows, tokens, patterns). Logistics and expectations: - Interviewers may include a Bar Raiser. Follow-up emails may request written narratives. Whiteboarding/portfolio reviews occur in separate rounds; this session remains strictly behavioural. - Be concise. Expect frequent interrupts for depth checks and alternative exploration.