Manager II, Config Deployments

About the role

Pinterest is seeking an Engineering Manager to lead the Config Deployments team. The team provides infrastructure platforms for feature flags, configuration management, and delivery. The role involves setting the direction, driving results for the team to build resilient infrastructure platforms for servicing engineers and the flagship product. The ideal candidate will have experience on teams responsible for high-scale distribution platforms which balance safety, velocity, and developer experience.

Responsibilities

• Manage and coach an engineering team building high-scale configuration distribution and service coordination systems
• Align strategy across stakeholders and translate customer needs into a realistic roadmap for mission-critical infrastructure
• Drive efficient execution, mitigate risks autonomously, and empower direct reports to lead
• Foster an inclusive and collaborative team environment
• Support thoughtful team growth
• Drive best practices in CI/CD, feature flags, and configuration management
• Use AI to accelerate planning, technical exploration, and decision-making across the team
• Draft strategy docs, compare architectural approaches, and synthesize research faster while applying judgment and verification to ensure correctness and quality
• Automate repeatable management tasks like status reporting, document synthesis, and review workflows, freeing the team to focus on higher-leverage work

Requirements

• 7+ years of engineering experience, including 5+ years in infrastructure
• 1+ years of engineering management experience
• Experience leading teams focused on improving developer experience through infrastructure
• A thoughtful leader who values efficiency and consistently delivers on expectations
• Strong written and verbal communication skills
• Bachelor’s degree in computer science, a related field, or equivalent practical experience
• Demonstrated ability to use AI to improve speed and quality in day-to-day work for relevant outputs
• Strong track record of critically evaluating and verifying AI-assisted work through testing, review, or other quality controls