Run Consistent Back-to-Back Interviews

Project Context

Google Cloud is piloting a new interview day format for DevOps Engineer candidates: four back-to-back rounds completed in a single day, with interviewers spread across Site Reliability Engineering, Google Kubernetes Engine, Security, and Hiring Committee prep. You are the program owner responsible for making the process consistent, fair, and scalable before it is rolled out to three hiring pods next quarter.

The pilot covers 60 candidates over 8 weeks, using 24 interviewers across Mountain View, Seattle, and Bengaluru. Leadership wants faster decisions without increasing interviewer load or creating inconsistent candidate evaluations between early and late rounds.

Key Stakeholders

Recruiting Operations wants shorter time-to-decision and fewer reschedules. Engineering interviewers want flexibility and low prep overhead. The hiring manager wants signal quality to remain high, while the People Operations partner is focused on fairness and calibration across interviewers and time zones.

Constraints

You have 6 weeks to design and launch the pilot, a budget of $45,000 for tooling and interviewer training, and no additional full-time headcount. The process must integrate with Google Workspace, Google Meet, and internal feedback workflows. Interviewers can spend at most 2 hours each on training and calibration.

Complications

1. Historical feedback quality is uneven: 30% of interview packets last quarter required follow-up due to vague or inconsistent scoring.
2. Two senior interviewers are unavailable during Weeks 4-5, when the first candidate wave begins.
3. Recruiting has already committed to reducing average decision time from 10 business days to 5.

Deliverables

1. Create a 6-week execution plan to launch the pilot with clear milestones and owners.
2. Define how you will keep interviewer evaluation standards consistent across back-to-back rounds.
3. Propose trade-offs if calibration, speed, and interviewer availability conflict.
4. Identify the top risks to launch quality and how you would mitigate them.
5. Define success metrics for the first 60-candidate pilot and your recommendation for scaling or adjusting the process.