Expert - EU RD

Job Description

The Reference Data Operations Expert is responsible for ensuring high-quality, timely and accurate Reference Data delivery across EU markets. The role combines operational execution, quality governance, stakeholder collaboration, process improvement and technical expertise to support end-to-end delivery—from input through to client delivery.

The role brings an AI-first, continuous-improvement mindset, identifying where AI, automation, analytics and intelligent controls can reduce manual effort, strengthen preventive quality checks and improve operational outcomes.

Key Responsibilities

• Execute and support high-quality Reference Data operations across EU markets, ensuring delivery meets agreed quality, timeliness and customer expectations. • Partner with operational teams to resolve process and technical challenges, ensuring new tools, system enhancements and AI-enabled solutions are understood and effectively adopted. • Identify repetitive, manual or high-risk activities and recommend AI, automation and process-improvement opportunities to enhance efficiency, scalability and quality. • Support the testing, implementation and continuous refinement of AI-enabled validation, workflow and decision-support solutions in partnership with Technology, Transformation and operational teams. • Lead error-management and Root Cause Analysis activities, providing clear issue analysis, corrective and preventive actions, and timely stakeholder communication. • Strengthen preventive quality controls through data analysis, trend identification, AI-supported validations and targeted review mechanisms. • Maintain effective communication with Client Ops teams and cross-functional stakeholders; provide accurate project updates and manage operational queries efficiently. • Define, monitor and document process execution, including standard operating procedures, governance controls, exception tracking and process documentation. • Ensure the accuracy and consistency of Reference Data coding is of high standards; investigate data-related concerns raised by clients and internal teams and support timely resolution. • Execute Reference Data operations and project activities with a clear understanding of the production lifecycle—from retailer/manufacturer input, coding and validation through to final client delivery. • Deliver assigned work accurately within agreed timelines, coordinating with local, regional and offshore teams to resolve data inquiries and operational challenges. • Support standardization and continuous improvement across methods, applications, reporting and operational workflows. • Provide technical and process expertise across Reference Data, supporting Western European markets in implementing projects, new capabilities and operational changes. • Prepare, maintain and interpret KPI reporting covering Quality Escapes, data accuracy, delivery performance, productivity and adherence to operational commitments. • Translate operational data into actionable insights, highlighting risks, trends and opportunities for improvement. • Support colleagues through knowledge sharing, process guidance and training on quality standards, new tools and responsible use of AI-enabled solutions. • Collaborate effectively in a virtual, multicultural environment across markets, functions and stakeholder groups. • 5–8 years of experience in Operations, Quality Management, Data Operations and/or client-facing roles. • Experience identifying and implementing process improvements, automation opportunities or technology-enabled operational enhancements. • Strong interest in applying AI, analytics and automation to improve operational performance, quality controls and customer outcomes. • Ability to critically review AI-generated recommendations, validate outputs against business rules and ensure appropriate human governance. • Fluent written and spoken English. • Strong analytical and problem-solving skills, with the ability to interpret data, identify trends and translate insights into practical actions. • Strong understanding of KPIs, operational monitoring, quality-performance management and root-cause analysis. • Ability to explain technical concepts, process changes and AI-enabled solutions to stakeholders with varied levels of expertise. • Process-oriented mindset with experience defining, refining and improving processes to strengthen quality, efficiency and on-time delivery. • Strong organizational, presentation and stakeholder-management skills. • Knowledge of process-improvement methodologies and project-management practices. • Ability to scope project requirements, manage stakeholder expectations and deliver against agreed objectives.