Section Head Data Projects Delivery - DCU

Responsibilities:

Core Responsibilities:

• Project Management: Lead and manage data and BI projects from initiation to completion, ensuring adherence to project goals, timelines, and budget constraints.

• Develop and implement a scalable framework for managing multiple complex data initiatives simultaneously.

• Project Planning and Execution: Develop comprehensive project plans, including defining project scope, deliverables, resource requirements, and timelines. Execute project plans, monitor progress, and take proactive measures to overcome obstacles or mitigate risks.

• Stakeholder Management: Collaborate with business stakeholders and executive leadership to understand project objectives, requirements, and priorities. Manage stakeholder expectations and ensure effective communication throughout the project lifecycle.

• Team Leadership: Manage and provide leadership to cross-functional project teams, including data engineers, data analysts, business analysts, and other relevant stakeholders. Foster a collaborative and high-performing team culture.

• Budget and Resource Management: Develop and manage project budgets, ensuring efficient allocation of resources and adherence to financial constraints. Coordinate with resource managers to secure necessary resources for project execution.

• Risk Management: Identify project risks and develop risk mitigation strategies. Monitor project risks and take proactive actions to minimize their impact on project outcomes.

• Quality Assurance: Ensure the delivery of high-quality data and BI solutions by defining and implementing quality control processes and conducting thorough testing and validation activities.

• Documentation and Reporting: Maintain comprehensive project documentation, including project plans, progress reports, and issue logs. Prepare regular status updates and reports for project stakeholders and leadership.

• Continuous Improvement: Identify opportunities for process improvement and implement best practices to enhance project delivery efficiency and effectiveness.

• Implement best practices in project management methodologies for data projects.

• Lead the adoption and optimization of modern data stack technologies.

• Collaborate with business stakeholders to understand data requirements and deliver actionable insights.

• Communicate project progress and outcomes to both technical and non-technical stakeholders.

• Work closely with business analysts and data engineers to acquire, clean, and transform data for reporting purposes.

Team Management:

• Build and lead a high-performing team, including hiring, training, and mentoring team members.

• Foster a culture of innovation, collaboration, and continuous improvement within the team.

Qualifications:

• Bachelor's degree in Computer Science, Information Systems, or a related field. A master's degree is a plus.

• Proven experience in managing end-to-end data and business intelligence projects, including project planning, execution, and delivery.

• Strong knowledge of data management principles, data warehousing, and business intelligence concepts.

• Proficiency in project management methodologies and tools, such as Agile or Scrum.

• Excellent leadership and team management skills, with the ability to motivate and guide cross-functional teams to achieve project goals.

• Strong communication and interpersonal skills, with the ability to effectively collaborate with technical and non-technical stakeholders.

• Solid understanding of data governance, data quality, and data security principles.

• Experience with data visualization tools, such as Tableau, Power BI, or QlikView, is a plus.

• Proven ability to manage multiple projects simultaneously and prioritize work effectively.

• Strong analytical and problem-solving skills, with a focus on delivering practical and actionable insights.

• Problem-solving mindset and the ability to thrive in a dynamic, fast-paced environment.

• Demonstrated success in leading large-scale, enterprise-wide data transformations.

• Familiarity with advanced analytics, machine learning, and artificial intelligence.