Data Support Lead

Overview: The Data Support Lead is responsible for overseeing master and client data operations, ensuring high-quality data delivery, and leading a team to execute against business priorities. This role drives process efficiency, manages backlog and project work, and partners across functions to improve data quality, scalability, and operational performance. Responsibilities: Lead and execute data support projects, ensuring quality, timelines, and stakeholder expectations are met. Define, track, and act on performance metrics to improve team output and effectiveness. Review and refine processes to improve efficiency, consistency, and scalability. Ensure adherence to standard operating procedures and maintain accurate documentation. Identify opportunities for automation and process enhancement. Coordinate work across teams and escalate issues appropriately. Align team activities with strategic goals and ensure progress is tracked and communicated. Develop team capabilities through training, mentoring, and performance management. Manage workload prioritization, staffing needs, and resources Requirements: Bachelor’s degree in Computer Science or related discipline or equivalent experience Minimum five years’ related experience in a Data Operations environment. Data quality management and governance. Analytical thinking and root cause problem solving. Operational execution and continuous improvement. Performance management and metrics-driven leadership. Project and backlog management. Effective cross-functional communication and influence. Technical proficiency with databases, productivity tools, and basic querying. Ability to identify and respond to emerging market trends and changes, ensuring master brand data reflects current industry and client dynamics Ability to drive continuous improvement in data quality and operational efficiency by identifying gaps, implementing controls, and enhancing validation processes Ability to lead backlog data cleanup efforts by prioritizing work, executing against defined project plans, and maintaining clear visibility into progress and risks Ability to actively manage team productivity through the consistent use of performance metrics, ensuring output meets established volume and timeliness expectations Ability to ensure delivery of high-quality data by maintaining strong accuracy standards and proactively reducing errors identified downstream or post-delivery