The behavioral part felt like a warm-up but it wasn't.
Use the STAR method to structure your answer, focusing on a project where you had clear ownership and cross-functional collaboration. Highlight the decisions you made, how you aligned stakeholders, and quantify the impact with metrics like latency reduction, cost savings, or user growth.
Pro tip: Emphasize how you navigated trade-offs and influenced without authority, as Uber values engineers who can drive alignment across teams. Quantify impact in terms of business metrics (e.g., increased driver utilization, reduced ETAs) to show you understand the bigger picture.
Briefly describe the project, its goals, and why it mattered to Uber. Mention the teams involved and your specific role.
Explain the key decisions you owned, such as architectural choices, prioritization, or resource allocation. Highlight how you drove alignment across stakeholders.
Describe how you executed the project, including any obstacles you overcame and how you kept cross-functional partners informed and engaged.
Share measurable outcomes using specific metrics (e.g., reduced latency by X%, saved $Y, increased conversion by Z%). Tie them to business goals.
Summarize key takeaways, what you would do differently, and how the experience prepared you for future challenges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the project's functional and non-functional requirements, then sketch a high-level architecture with clear component boundaries and data flow. Walk through the design as load increases 10x, explaining where bottlenecks occur and what scaling decisions you'd make (e.g., horizontal scaling, caching, sharding). Emphasize trade-offs and justify each choice with metrics or reasoning.
Pro tip: Quantify the impact of scaling decisions: e.g., 'Adding a cache reduces DB load by 80% based on our access pattern.' This shows you think in terms of measurable outcomes, not just theoretical solutions.
Ask questions to understand the project's scope, expected load, latency requirements, and data consistency needs. This ensures your design targets the right problems.
Draw the main components (e.g., clients, load balancers, services, databases, caches) and show how data flows between them. Keep it simple and focus on the core path.
Analyze each component for potential failure points under increased load, such as database contention, network latency, or service saturation.
For each bottleneck, describe specific scaling techniques (e.g., horizontal scaling, sharding, caching, async processing) and explain the trade-offs.
Recap the scaled architecture, highlighting how it meets the 10x load requirement, and invite feedback or discuss alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.