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