In an Amazon L6 Senior Program Manager interview, you may be asked questions about customer feedback, fast decisions with limited data, and deep dives. Prepare a structured answer to prompts such as: ### Constraints & Assumptions - Show customer obsession and analytical rigor without becoming slow or indecisive. - Distinguish reversible decisions from hard-to-reverse decisions. - Use data, customer evidence, and operational judgment together. - Include success metrics, guardrails, and rollback criteria. ### Clarifying Questions to Ask - What customer segment or program are we making the decision for? - What is the decision deadline, and what happens if we wait? - Is the decision reversible, partially reversible, or hard to reverse? - What data sources are available immediately? ### Part 1 - Gather Customer Feedback What methods have you used to gather customer feedback? #### What This Part Should Cover - Direct feedback such as interviews, surveys, usability tests, beta groups, advisory councils, or field visits. - Indirect feedback such as support tickets, app reviews, NPS comments, account-manager notes, and escalations. - Behavioral feedback such as funnel data, retention, cancellations, search logs, usage patterns, and operational metrics. - Triangulation to avoid over-weighting a biased source. ### Part 2 - Make A Quick Decision With Limited Data Tell me about a time you had to make a decision quickly without enough data. #### What This Part Should Cover - The decision, deadline, uncertainty, and stakes. - The fastest high-signal evidence you gathered. - Reversible versus irreversible framing, decision criteria, action, result, and learning. ### Part 3 - Deep Dive To Solve Problems How do you deep-dive to solve problems? #### What This Part Should Cover - A method: metric tree, segmentation, instrumentation validation, 5 Whys, system tracing, customer evidence, and root-cause isolation. - How you separate symptoms from causes. - Prevention mechanisms after the fix. ### What a Strong Answer Covers - Acts quickly without being reckless. - Uses customer signals, data, and judgment together. - Makes assumptions explicit and defines guardrails. - Shows how learning continues after the decision. ### Follow-up Questions - What data did you wish you had? - What would have made the decision a one-way door? - What customer feedback source was most reliable? - How did you know the root cause was fixed? - What rollback criteria did you define?