Pulled out a story I'd prepped but fumbled the 'why it was innovative' part.
Use the STAR method to describe a specific situation where you identified a problem, proposed an innovative solution, and implemented it successfully. Focus on your thought process, the innovation itself, and the measurable impact, aligning with Amazon's Leadership Principles like Customer Obsession and Invent and Simplify.
Pro tip: Quantify the impact of your solution (e.g., reduced latency by 30%, saved $X) and explicitly tie it to an Amazon Leadership Principle to show cultural fit.
Briefly describe the situation and the problem, including any constraints or ambiguity. Highlight why the problem was significant.
Detail the specific challenge you faced, such as technical limitations, tight deadlines, or unclear requirements, to show the complexity.
Walk through your thought process and the innovative solution you devised. Emphasize creativity, technical depth, and how you overcame obstacles.
Explain how you implemented the solution, any collaboration involved, and the measurable results (e.g., performance improvements, cost savings, customer impact).
Summarize key learnings and explicitly connect your actions to Amazon's Leadership Principles, such as Customer Obsession or Invent and Simplify.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Choose a project where you actively used metrics to drive decisions, not just report them. Clearly define your north star metric, justify why it best captured user value and business impact, and explain how you used it to prioritize engineering work and measure success.
Pro tip: Amazon values customer obsession and measurable impact, so tie your north star metric to a customer outcome (e.g., reducing latency improves conversion) and show how you avoided vanity metrics. Also, briefly mention a trade-off you made when choosing it over other plausible metrics.
Briefly describe the project, your role, and the business or customer problem it addressed. Keep it concise so you can spend most time on metrics.
State the metric clearly and explain why it was the best proxy for customer value and business success. Mention alternatives you considered and why you rejected them.
Describe how you instrumented, tracked, and iterated on the metric. Give a specific example of a decision or trade-off you made based on the metric.
Share the before-and-after numbers, and connect the metric improvement to tangible outcomes like revenue, retention, or efficiency.
Summarize what you learned about choosing and using metrics, and how you'd apply that at Amazon.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Did not see a full case question coming in a SWE interview.
Start by framing the problem around the seller's needs and Amazon's strategic goals, then propose a phased GTM and product strategy that balances quick wins with long-term scalability. Emphasize data-driven decisions, local market nuances, and a feedback loop between product and GTM.
Pro tip: Show you understand that in Brazil, trust and local partnerships are critical; propose starting with a pilot in a key region and using seller feedback to iterate quickly. Also, highlight how you'd measure success with metrics like seller acquisition cost, loan uptake, and repayment rates.
Research the Brazilian e-commerce landscape, seller pain points, and regulatory environment. Identify target seller segments and their financing gaps.
Design a lending product that fits seller cash flow cycles, with flexible repayment options and risk-based pricing. Leverage Amazon's seller data for underwriting.
Choose a phased rollout: pilot in a high-density seller region, then expand. Use Amazon's seller ecosystem for acquisition and partner with local financial institutions for capital and compliance.
Set competitive interest rates and fees, considering risk and market benchmarks. Explore revenue-sharing with partners and value-added services.
Define KPIs (e.g., loan volume, default rate, seller growth) and establish a feedback loop to refine product and GTM continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.