Description

PXT Central Science (PXTCS) is seeking a driven, results-oriented Senior Manager of Data Engineering to lead and grow a high-performing team. We are looking for a leader who thrives in a fast-paced, high-ambiguity environment, relishes working at scale, puts big data to work, and above all else, is passionate about building teams and developing talent.

PXTCS applies science to improve the employee and candidate experience across Amazon's People, Experience, and Technology (PXT) organization. In this role, you will own the data engineering vision, strategy, and execution that powers insights and products enabling Amazon to Hire and Develop the Best.

Key job responsibilities
- Own the data engineering product vision and roadmap for your organization, aligning to PXTCS strategic goals
- Drive architectural design choices for scalable, reliable, and performant data infrastructure and services
- Lead the design and development of data pipelines, data models, and data warehouse strategy that aggregate data from multiple sources and expose actionable insights
- Ensure the highest standards of quality, maintainability, performance, and availability across all data systems
- Build, lead, mentor, and develop a team.
- Recruit and retain top talent; foster a culture of ownership, innovation, and continuous learning
- Partner closely with scientists, applied scientists, economists, and PXT business leaders to understand data needs and deliver solutions that accelerate decision-making
- Propose and implement business metrics for senior leadership reviews
- Perform deep-dives to identify root causes behind variances in key parameters and surface compelling, data-driven recommendations
- Triage competing priorities in a high-ambiguity environment using both quantitative analysis and business judgment
- Work across PXTCS and PXT partner teams to expose data pipelines and enable self-service analytics
- Develop intelligent, insightful self-reporting tools that empower stakeholders with actionable data
Report results in a manner that is both statistically rigorous and compellingly relevant to business stakeholders