Senior Manager, Data Engineering

Company Description

About the Organization 

KIPP Public Schools is a national network of tuition-free public charter schools that prepares students with the skills and confidence to create the future they want for themselves, their communities, and us all. We are a network of 279 schools, nearly 16,500 educators, and nearly 210,000 students and alumni.

Our mission: Together with families and communities, we create joyful, academically excellent schools that prepare students with the skills and confidence to pursue the paths they choose—college, career, and beyond—so they can lead fulfilling lives and build a more just world. 

Our vision: Every child grows up free to create the future they want for themselves and their communities. 

The KIPP Foundation is a nonprofit organization that supports KIPP public charter schools across the country. The KIPP Foundation helps to develop outstanding educators to lead KIPP schools; provides tools and resources for excellent teaching and learning; promotes innovation; and facilitates the exchange of insights and ideas across KIPP and other public schools and organizations. 

About Our Values: 

Excellent Results for Students: We value results over effort and know that our work is not finished until all students thrive. We maintain high expectations for self, team, and students. We drive outcomes by operating with agility, removing barriers, differentiating support, and prioritizing student-centered solutions—especially for those at the margins. 

Collective Impact: We are one team. We win together. We embody a one-team mindset by working collaboratively across teams, aligning goals, and leveraging the strengths of all teammates. We build trust-based relationships and ensure inclusive decision-making by engaging those closest to the work. We provide clarity through simple, repeatable systems and goals that enable transformational outcomes. 

Courageous Action: Our belief in what we can achieve is unwavering, fueling our optimism and commitment to overcome any challenge. We demonstrate an unwavering belief in students' potential and a commitment to addressing systemic inequities. We approach challenges with resilience, a solutions-focused mindset, and adaptability. We foster a psychologically safe environment centered on trust and accountability where feedback is encouraged. We leverage data to reflect on progress, own setbacks, celebrate successes, and continuously improve to maximize impact. 

Job Description

About The Position 

This role is responsible for creating, managing, and optimizing data engineering pipelines, managing data platform operations, and partnering closely with stakeholders to drive requirements-gathering and defining data solution architecture. This is a hands-on role that creates, drives, and tracks project plans for data platform and solutions delivery (including, but not limited to, ETL/Extract-Transform-Load, pipelines, data models, and monitoring controls) and leads sprint planning and completion to meet business needs while implementing and continuously improving solutions. The Senior Manager, Data Engineering reports to the Senior Director of Data Management & Governance. 

Responsibilities 

• Own and drive the end-to-end delivery of data architecture and pipeline solutions, including holistic planning, requirements-gathering, design, build, and testing strategy per assessed business needs. Design and evolve the semantic layer to be the well-governed single source of truth for business intelligence and ensure it is meeting all reporting needs. 

• Lead sprint planning and operations. Manage sprint execution, release planning and release delivery, ensuring aligned planning, timely outcomes, and resolution of dependencies and blockers to drive operational excellence. Track and analyze sprint delivery metrics, sprint operational efficiency, and DevOps performance (e.g., DevOps Research and Assessment framework) to support continuous improvement. 

• Design and operationalize data quality framework to ensure trust and consistency in data, with proactive monitoring, alerting, root cause analysis (including troubleshooting and resolution) with impact and sizing assessments for all critical data pipelines. 

• Establish foundational capabilities for MLOps (Machine Learning Operations), Machine Learning, and feature engineering along with ML/AI platform integration as organizational needs mature.  

• Manage and continuously improve data architecture and pipelines, establishing clear monitoring standards and operational controls. 

• Partner closely with stakehol