Senior Manager, Data Engineering

About this role

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.

Skills and Mindsets

• Mission and Student Focus: Demonstrates passion and commitment to KIPP’s mission and possesses the desire and ability to uphold KIPP’s Core Values (Focus on Excellent Results, Collective Impact, and Courageous Action)

• Self-Management: Demonstrated record of co-creating ambitious goals with their managers – and driving toward desired outcomes; Effectively monitors progress toward goals for multiple workstreams; Leverages understanding of an organization’s operating model, structure, and core operational process to effectively drive work to achieve goals; effectively works through direct reports and peers to follow through on commitments, ensuring others do the same.

• Work Management: Effectively drives work they own through clear action planning. Manages time effectively around competing priorities. Skilled in creating and executing project plans through partnership with stakeholders and effective delegation to achieve intended results on time. Skilled in knowledge management.

• Process Management: Able to build and align goals and project plans with organization priorities; leverages relevant organizational processes and systems to execute work; can build and maintain knowledge management systems; effectively develops and codifies processes to ensure efficiency, alignment with organizational processes, and to improve work quality.

• Problem-Solving: Generates solutions outside of their direct work and shrinks problems to solve independently. Shows initiative in generating and recommending ideas and solutions within their own workstreams. Anticipates risks and develops proactive solutions that support their team.

• People Management & Development: Effective at developing and coaching teammates. Skilled in setting and managing both performance and development goals. Provides timely and actionable feedback to drive teammate development and performance; demonstrates accountability for the goals of their direct reports

• Technical Proficiency: Has knowledge and skills in SQL, Spark, Pyspark, on-prem and cloud-based data platform management (SQL Server, Fabric/Snowflake, etc.) and architecture, CI/CD, ETL/data pipelines, version control, SSIS, agile sprint operations, project management tools, and machine learning principles.

Experience and Qualifications

• 8+ years of professional experience in data engineering, infrastructure engineering, or a closely related technical field.

• Experience with SQL-based data platforms (such as SQL Server) and modern cloud-based data warehouse/lakehouse platforms, such as Fabric/OneLake, Snowflake, or Databricks.

• Demonstrated experience in managing agile sprint operations, DevOps metrics, and proficiency in JIRA (or similar tool) for project and sprint planning, sprint and backlog management, sprint tracking, workflow management, and issue resolution.

• Experience with version control and CI/CD workflows using Git and platforms, such as Bitbucket, GitHub, and Azure DevOps.

• Strong technical proficiency in SSIS, Spark, PySpark, and SQL.

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