Got the basic loop-through-a-list-of-conditions approach working, which felt fine in the moment.
Start by clarifying requirements and use cases, then design a core API with a filter interface that can be extended via composition or inheritance. Discuss trade-offs between different extensibility mechanisms and how to handle performance and scalability.
Pro tip: Emphasize that extensibility should not come at the cost of simplicity for common cases; show how to keep the API intuitive while allowing advanced customization.
Ask questions to understand the scope: what file systems, expected scale, performance needs, and whether filters should be combinable (AND/OR).
Design the main entry point (e.g., findFiles) and a Filter interface with a method like matches(FileMetadata). Include basic filters for size and type.
Choose an extensibility pattern: e.g., plugin architecture, strategy pattern, or allowing custom predicates. Discuss how new filters can be added without modifying core code.
Consider how filters are evaluated (e.g., lazy evaluation, short-circuiting), and how to optimize for large directory trees (e.g., parallel traversal, indexing).
Compare approaches: e.g., simple inheritance vs. composition, built-in filters vs. user-defined, and how to balance flexibility with ease of use.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was actually the round I felt best about.
Start with a concise 60-90 second overview of your resume, highlighting roles and key skills. Then select one technical project or decision that aligns with Amazon's Leadership Principles, and dive deep using the STAR method, emphasizing technical trade-offs, data-driven decisions, and measurable impact.
Pro tip: Amazon interviewers value depth over breadth. Choose a project where you can clearly articulate the problem, alternatives considered, why you chose your solution, and the results—demonstrating customer obsession and ownership.
Provide a brief chronological summary of your career, focusing on roles, responsibilities, and key achievements that are relevant to the Software Engineer role at Amazon.
Choose a technical project or decision where you played a significant role, ideally one that showcases technical complexity, trade-offs, and alignment with Amazon's Leadership Principles.
Describe the situation, the problem to solve, and your specific responsibility. Include any constraints such as time, resources, or scalability requirements.
Explain the technical alternatives you considered, the criteria you used to evaluate them (e.g., performance, cost, maintainability), and why you chose your approach. Highlight any trade-offs made.
Quantify the impact of your work (e.g., performance improvements, cost savings, user growth) and reflect on what you learned or would do differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
LP questions came up in every round and were consistently the part I felt most confident about.
Use the STAR method to structure your answers, focusing on specific past situations where you navigated ambiguity or resolved conflict. Emphasize your actions, the reasoning behind them, and the measurable impact, while aligning with Amazon's Leadership Principles.
Pro tip: Quantify results whenever possible and explicitly connect your story to Amazon's Leadership Principles like 'Customer Obsession' or 'Ownership' to show cultural fit.
Briefly describe the situation, including the project, team dynamics, and the specific challenge related to ambiguity or conflict.
Clearly state the ambiguity or conflict, and why it was important to resolve it for the project's success.
Explain the steps you took to address the situation, highlighting your thought process, collaboration, and leadership.
Share the results, including quantifiable metrics and lessons learned, and how it benefited the team or company.
Explicitly tie your actions and outcomes to relevant Amazon Leadership Principles to demonstrate alignment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.