This came up constantly across multiple companies, basically a deep dive into something real I'd built.
Choose a service you built end-to-end that had clear requirements, technical challenges, and measurable outcomes. Structure your answer as a story: context, problem, design decisions, implementation, and results. Focus on trade-offs and your specific contributions to show depth and ownership.
Pro tip: Quantify the impact of your service (e.g., latency reduction, cost savings, scalability improvements) and explicitly state the trade-offs you considered. This demonstrates business acumen and engineering maturity.
Briefly describe the business problem, the team you were on, and the high-level requirements for the service. Keep it concise to focus on the technical details.
Walk through the architecture, key components, and the trade-offs you evaluated (e.g., consistency vs. availability, SQL vs. NoSQL, monolith vs. microservices). Justify your choices.
Highlight the technologies used, your specific role, and any challenges you overcame during coding, testing, or deployment. Mention collaboration with other teams if relevant.
Quantify the outcomes: performance metrics, cost savings, user adoption, or reliability improvements. Explain how the service benefited the business.
Discuss what you would do differently and how this experience shaped your approach to system design. This shows growth and self-awareness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by thoroughly understanding the codebase and the reported issue, then systematically trace the bug using debugging tools and code reading. After identifying the root cause, propose and implement a fix, ensuring it doesn't introduce regressions. Finally, address the modification request by integrating it cleanly, considering trade-offs and testing.
Pro tip: Always communicate your thought process and ask clarifying questions before diving in; this demonstrates collaboration and prevents solving the wrong problem. Also, write tests to reproduce the bug and validate the fix, showing professionalism and attention to quality.
Explore the project structure, read documentation, and identify the relevant modules. Clarify the bug report and the modification request to ensure you understand the expected behavior.
Set up the environment, run the code, and reproduce the bug. Use debugging tools, logs, and breakpoints to trace the root cause, considering edge cases and recent changes.
Write a failing test that captures the bug, then fix the code with minimal changes. Run tests to confirm the fix and check for regressions.
Analyze the modification request, design a solution that integrates with the existing code, and implement it. Consider trade-offs like performance, readability, and maintainability.
Add tests for the new functionality, run the full test suite, and review your changes for quality. Document any assumptions or decisions made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints and edge cases first, then discuss a brute-force solution before optimizing with appropriate data structures or algorithms. Walk through your thought process, analyze time and space complexity, and test your solution with examples.
Pro tip: Always state the time and space complexity of your solution and mention potential trade-offs; this shows you think like an engineer, not just a coder. Also, consider writing clean, modular code with meaningful variable names, as AT&T values maintainable solutions.
Ask clarifying questions about input size, constraints, edge cases, and expected output format. Restate the problem in your own words to confirm understanding.
Start with a brute-force solution, then identify bottlenecks and propose optimizations using appropriate data structures or algorithms. Discuss trade-offs.
Select the best approach based on constraints, and clearly explain why it's optimal. Outline the steps of your algorithm before coding.
Write clean, modular code, and test with provided examples and edge cases. Verbally walk through your code to catch errors.
State the time and space complexity, and discuss any potential improvements or alternative solutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.