Manager, Data Analyst

University degree - preferably in business management, data studies or comparable course of studies Fluent written and verbal skills (conversational and business communications) in English Lean Six Sigma Green Belt or alternative would be considered a strong advantage Analytical Thinking - Intermediate Relationship Management - Advanced Finance Operations and Accounting - Foundational Design Thinking - Intermediate Project Management - Intermediate IT System Landscape - Advanced Previous work experience within a central Data / Supply Chain/ Finance organization with focus on process excellence & decision making Excellent knowledge of MS Excel required (advanced) ERP experience essential, preferably SAP Mastery of Material Master & Production Execution data areas Strong Data Mindset & Passion for Data Demonstratable understanding of Market-to-Order & Supply Chain Processes and the data solutions & systems enabling them would be considered an advantage Process & Operational Excellence experience and approach Demonstratable strong influencing skills Excellent communication & interpersonal skills Keen to share his knowledge with others, strong people development mindset Demonstrable relationship building experience withing a multinational environment. You Thrive working with people Curious and problem-solving nature Ability to confidently make rapid decisions in a dynamic business environment Very strong time management, planning and organizing skills Can-do attitude & proactivity mindset Technical acumen - you are able to quickly pick up new systems, tools including Advanced excel. Eagerness to learn, experiment and not be afraid of failure Analytical skills Attention to details Technical acumen - you are able to quickly pick up new systems, tools including Advanced excel. Attention to details It includes data quality, governance compliance, load execution, issue resolution, and continuous process improvement within the Forecast-to-Load ecosystem. This involves being able to understand the business needs of the different business areas, the processes and data elements required by SAP and other systems to support these needs within the country while reflecting specific customer & legal requirements within the end design. Context: extremely dynamic and versatile multi-cultural business. Manages escalations coordinating communication with senior management & external partners as relevant Leads the design & executes training plan for new team members as well as drives upskilling opportunities amongst existing team/s to improve expertise & performance First line of contact for escalations related to the Forecast to load data objects pipeline Support the NPI Lead with workload distribution, performance assessment & recruitment Support and/or lead on change management initiatives, deployment of new services etc. Partake and be involved actively on strategic discussions related people, systems, process & data Identify process & operational opportunities for improvement and drive a plan for their application Supplies advice and/or acts as a Lead data business SME towards highly complex or high-risk initiatives Lead analysis/audit to ensure accuracy of Data and put action plan in place to correct faulty data. Lead to resolution and communicate any issues with relevant stakeholders Support/drives SAP and other technical/business initiatives through solution data design, delivery, and migration Works and liaises closely with ISS NPI and other key stakeholders Act as a role model regarding Team and Company values & culture Create, maintain, and validate material master records in SAP systems Manage material attributes Handle lifecycle management of the different data objects in scope (creation, extension, changes, blocking, and deletion) Ensure accuracy, completeness, and consistency of master data across plants, warehouses, and sales organization Supports new product launches (NPD/NPI) by ensuring timely and correct master data setup Monitor and resolve data inconsistencies, duplicates, and errors Enforce master data governance policies, naming conventions, and approval workflows