Data Support Lead (Brand Research Support)

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