Automation and Catalogue Specialist

About the Company

Responsibilities

• ● connected and ready for use, while partnering with business, platform and technology stakeholders to simplify processes, translate opportunities into scalable digital solutions, and demonstrate measurable business outcomes from implementation through adoption and benefit tracking.
• ● What you will be accountable for
• ● Catalogue foundation. Own and improve the article, service, supplier and commercial data that supports a consistent buying experience.
• ● Shared data language. Develop and maintain taxonomy, attributes, controlled vocabulary, standards, and cross-system mappings.
• ● Automation opportunity pipeline. Identify and assess process automation, AI and agentic AI opportunities using evidence from process performance, volumes, and pain points.
• ● Solution delivery. Translate validated business needs into requirements, solution designs, test scenarios, implementation plans, and measurable outcomes with platform and technology teams.
• ● Value-realization. Build credible baselines and value cases, define benefit measures, track adoption and outcomes, and provide transparent reporting to support prioritization and investment decisions.
• ● Stakeholder outcomes. Partner across category management, procurement operations, supply chain, suppliers, platform and technology teams to achieve sustainable adoption and resolve issues at source.
• ● Classify content against the agreed taxonomy and attribute model, resolving classification questions and exceptions with category and business stakeholders.
• ● Maintain traceable mappings between catalogue objects and identifiers used in ERP, purchasing and other connected systems across business units and entities.
• ● Coordinate supplier catalogue onboarding from submission guidance and validation through reconciliation, upload, publication and ongoing maintenance. 2. Data governance, quality and connectivity Define and maintain master data terminology, standards, validation rules, attributes, permissible values, ownership, validity and change history.
• ● Apply and improve data-quality controls, monitor exceptions and recurring error patterns, coordinate remediation and report the health of catalogue data.
• ● Work with technology teams on catalogue integrations and data exchange, including API-based connectivity, requirements, test scenarios, and validation.
• ● Design practical approaches for complex data scenarios where current system capability is limited, balancing control, usability, and scalability. 3. Automation, AI and process transformation Identify automation, AI, intelligent automation, and agentic AI opportunities through process analysis, stakeholder discovery, and operational data.
• ● Simplify and standardize processes before automation, remove avoidable complexity, and help define the target operating approach.
• ● Translate approved opportunities into clear business requirements, user stories, solution concepts, controls, test cases, implementation plans, and acceptance criteria.
• ● Partner with AI platform product owners, technology teams and business users through process design, testing, deployment, change management and stabilization.
• ● Perform structured root-cause analysis on catalogue and automation issues, then implement preventive and scalable improvements. 4. Value realization and performance management Establish pre-implementation baselines using relevant measures such as transaction volumes, handling effort, cycle time, cost, quality, compliance, exception rates and scalability.
• ● Identify and quantify value levers including productivity, cost avoidance, improved data quality, reduced rework, faster turnaround, increased automation coverage, improved user experience, and stronger process control.
• ● Develop evidence-based value cases that set out expected benefits, assumptions, dependencies, implementation costs, risks and measurement methods for prioritization and investment decisions.