Role Snapshot
Senior Manager of Data Engineering at KIPP Foundation responsible for designing, building, and optimizing end-to-end data architecture and pipeline solutions that serve as the organization's single source of truth for business intelligence. This hands-on leadership role drives project delivery, manages sprint operations, and ensures data quality and governance across the enterprise.
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 stakeholders on the Analytics, Application Development, Data Collection Strategy & Operations, IT Operations, Product Management, and Regional Data Systems & Strategy teams to lead communication and own, create, drive, track, and execute project plans for owned workstreams, effectively managing competing priorities to ensure data availability and accuracy for users.
Ensure transparency into progress, risks, and dependencies through consistent tracking and communication mechanisms.
Guide team member(s) on developing data solutions, promoting strong data quality practices and review standards, and ensuring up-to-date documentation.
Manage and develop one or more team members (FTEs or contractors) as needed, providing clear expectations, timely feedback, and coaching to support performance and accountability for results.
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