Manager Data Control and Data Science

Data Strategy & Architecture:
Define and implement a robust data strategy aligned with business objectives, including data collection, storage, integration, and governance.
• Data Warehousing & Lakehouse: Oversee the design, development, and maintenance of data warehousing and lake house solutions on AWS or Google Cloud, ensuring scalability, performance, and data integrity.
• Data Modeling & Governance: Establish and enforce data modeling standards, best practices, and governance frameworks to ensure data quality, consistency, and compliance.
• Advanced Analytics & Insights: Lead the development and deployment of advanced analytics models, including data mining, big data analysis, and machine learning, to uncover actionable insights and drive business value.
• Stakeholder Collaboration: Partner with business stakeholders to understand their data needs and translate them into actionable data science projects and solutions. Communicate complex technical findings in a clear and concise manner to both technical and non-technical audiences.
• Technology & Innovation: Continuously evaluate and adopt cutting-edge technologies in the data science and cloud computing domains to maintain a competitive advantage.
Digital Transformation Projects:
• Technology & Innovation
• Continuously evaluate and adopt cutting-edge technologies in the data science, cloud computing, and digital transformation domains to ensure the company stays ahead of the curve.
• Drive the implementation of innovative data-driven solutions that enable new business models, enhance customer experiences, and improve operational efficiency.
Data Analytics & Insights for Digital Transformation:
• Lead the development and deployment of advanced Data analytics models, including data mining, big data analysis, and machine learning, to support digital transformation initiatives.
• Identify key performance indicators (KPIs) and develop dashboards to track and measure the impact of digital transformation efforts.
• Provide insights and recommendations to guide strategic decision-making and optimize business processes.
Change Management:
• Develop and manage a centralized change management process for Data initiatives.
• Ensure all changes are thoroughly tested, documented, and communicated to stakeholders.
• Monitor and control change requests to minimize disruption data structure and ensure successful implementation.
• Conduct change impact assessments and develop mitigation strategies.
Change Control:
• Establish change control procedures to manage Data changes effectively.
• Ensure changes are aligned with business objectives and do not compromise security or compliance.
• Maintain a change control log and ensure proper documentation for all changes.
• Conduct post-implementation reviews to assess the success of changes and identify areas for improvement.
Data Modeling & Governance:
• Establish and enforce data modeling standards, best practices, and governance frameworks to ensure data quality, consistency, and compliance throughout the digital transformation journey.
• Champion data-driven decision-making and promote a culture of data literacy across the organization.
• Work closely with business leaders and cross-functional teams to understand their digital transformation goals and translate them into actionable data science projects.
• Communicate complex technical findings in a clear and concise manner to both technical and non-technical stakeholders, fostering understanding and buy-in.
User Training and Support:
• Train end-users on how to use dashboards and interpret the data presented.
• Provide ongoing support and enhancements to dashboards based on user feedback.
• Develop documentation and training materials for dashboard users.
Team Management:
• Lead, mentor, and develop a high-performing IT governance and PMO team.
• Set performance goals and conduct regular performance reviews.
• Foster a collaborative and innovative team culture across all IT functions
• Provide coaching and support to team members to help them achieve their career goals.
Professional Development:
• Team Leadership & Development: Foster a collaborative and high-performing team culture. Provide mentorship, guidance, and opportunities for professional growth to data scientists and analysts.
• Encourage a culture of compliance, governance, and accountability within the team.
• Identify training needs and provide opportunities for skill development for all Data team members & system users.
• Stay updated on industry trends and best practices to ensure the team remains competitive and knowledgeable.

Other

Data governance, Big Data Processing, Cybersecurity assessment, Leadership Skills, Programming / Vba / Python