I went straight to the usual suspects: comp, growth, manager quality.
Start by acknowledging that attrition is a complex issue with multiple drivers, and that a data-driven, empathetic approach is key. Then outline a structured plan: diagnose root causes, prioritize high-impact areas, implement targeted solutions, and measure progress. Emphasize collaboration with stakeholders and a focus on both quick wins and long-term cultural changes.
Pro tip: Tie your answer to Revolut's fast-paced, high-performance culture: show you understand that reducing attrition isn't about lowering the bar but about creating an environment where top talent thrives and wants to stay. Mention specific engineering retention levers like meaningful work, growth opportunities, and reducing friction in tools and processes.
Gather quantitative data (exit interviews, attrition rates by team/tenure) and qualitative feedback (surveys, 1:1s) to identify why people leave. Segment by role, performance level, and tenure to uncover patterns.
Work with engineering leads, HR, and cross-functional partners to prioritize the most impactful and actionable drivers. Align on shared goals and success metrics, ensuring efforts support business objectives.
Launch initiatives addressing top drivers: e.g., career development paths, mentorship, recognition programs, workload balancing, and improving tooling/processes to reduce frustration. Tailor to different segments as needed.
Track leading indicators (engagement scores, internal mobility, eNPS) and lagging indicators (attrition rate). Use A/B testing or pilot programs to validate impact, and adjust strategies based on results.
Embed retention into daily management: regular check-ins, transparent communication, and empowering engineers with autonomy and purpose. Celebrate successes and learn from failures to continuously improve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the decision context and the specific metrics that matter, then outline a balanced set of data points across user behavior, system performance, and business impact. Emphasize how you would validate the data and use it to drive the decision, while acknowledging trade-offs and potential biases.
Pro tip: Tie every data point back to a clear hypothesis or decision criterion, and mention how you'd avoid common pitfalls like survivorship bias or vanity metrics—this shows you think like a product-minded engineer.
Ask clarifying questions to understand what decision is being made and what success looks like. Define the key metrics that will indicate whether the decision was correct.
List the data sources available (e.g., product analytics, logs, A/B tests, user feedback) and select the most reliable and relevant ones for this decision.
Choose a mix of leading indicators (e.g., click-through rate, error rates) and lagging indicators (e.g., retention, revenue) to get a complete picture.
Check data quality, consider segmentation (e.g., by user cohort, platform, geography), and look for statistical significance to avoid misleading conclusions.
Explain how each data point supports or challenges the decision, and describe how you would weigh them to make a final call.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.