NCDIT - Data Quality IT Project Manager

Track and coordinate project tasks across internal and external cross-functional teams (data quality analysts, developers, business stakeholders). - Lead medium-sized, complex projects or multiple small projects, including those with medium risk and impact.

Job Summary (Data Quality IT Project Manager)

- Plan, execute, and oversee data quality projects to ensure they meet scope, time, cost, and quality objectives.
- Develop and maintain detailed project plans, timelines, and status reports.
- Track and coordinate project tasks across internal and external cross-functional teams (data quality analysts, developers, business stakeholders).
- Identify, communicate, manage, and mitigate project risks and issues.
- Support documentation, continuous improvement, and change management for data quality management processes.
- Lead medium-sized, complex projects or multiple small projects, including those with medium risk and impact.
- Provide day-to-day planning, organization, and coordination of team members' work.
- Multi-task effectively; demonstrate strong written and oral communication skills.
- Apply project management leadership to resolve technical issues and facilitate team collaboration.
- Translate and communicate technical concepts to both technical and non-technical audiences.
- Utilize knowledge of project management processes, documentation, checklists, and reporting criteria.
- Conduct detailed planning, project testing, risk assessments, and implement mitigation actions.
- Design and manage technical projects using quality assurance practices to ensure timely, on-budget delivery of expected results and benefits.
- Develop reports and graphical presentations for technical and non-technical audiences.
- Use Microsoft Office (Word, Excel, PowerPoint) and Project for documentation and reporting.
- Apply strong knowledge of data governance, data management, and data quality frameworks, methodologies, and best practices.
- Demonstrate proven experience as a project manager, preferably in data quality or data governance projects.