Senior Data Engineer, Microsoft Fabric & AI

Role Summary

GS1 Global Office is seeking a Senior Data Engineer, Microsoft Fabric & AI to join the Business Intelligence and Data Analytics team. The role will design, build and support secure, scalable enterprise data and analytics solutions using Microsoft Fabric, Azure data services and Power BI. It will also develop AI-enabled data pipelines that connect approved enterprise data with large language model platforms, including Claude AI and other approved enterprise AI tooling.

This is a senior, hands-on individual-contributor role combining data engineering, business intelligence, AI-enabled solution development and production support. The role will provide technical leadership, establish reusable engineering standards and work with stakeholders across GS1 Global Office and Member Organisations to translate business needs into reliable, well-governed data products.

Responsibilities

Data engineering and architecture

• Design, develop and maintain scalable data pipelines using Microsoft Fabric and Azure data services.
• Build and support Lakehouse, Data Warehouse and semantic model solutions using appropriate architecture and reusable design patterns.
• Develop reliable data integration processes using Microsoft Fabric Data Pipelines, Azure Data Factory, APIs and other approved integration methods.
• Support migration and modernisation initiatives involving Microsoft Fabric and Azure analytics services.
• Optimise data processing performance, scalability, maintainability and cost efficiency.
• Contribute to enterprise data architecture decisions and the evolution of shared data models and analytics standards.

AI-enabled data solutions

• Design and build retrieval-augmented generation pipelines that securely connect approved enterprise data to Claude AI and other approved large language model platforms.
• Develop data preparation, chunking, embedding, indexing and retrieval processes that support accurate and traceable AI-enabled search and analytics.
• Support the responsible adoption of approved enterprise AI and development tools, including Claude AI and Claude Code, with secure data-handling and access patterns.
• Evaluate and prototype AI-enabled analytics use cases with senior leaders and technical stakeholders, considering business value, architecture, risk and guardrails.
• Monitor and improve AI pipeline reliability, quality, performance and cost in line with GS1 governance standards.
• Maintain appropriate documentation, traceability and human oversight for AI-enabled solutions.

Business intelligence and reporting

• Develop and maintain Power BI dashboards, reports, datasets and semantic models that provide clear and actionable insights.
• Support operational reporting, data-quality reporting and prioritised ad hoc analytics requests.
• Work with business stakeholders to understand requirements, define acceptance criteria and translate needs into sustainable reporting solutions.
• Promote consistent definitions, measures and reporting practices across the organisation.

Data quality, security and governance

• Implement data validation, observability, monitoring and quality controls across data pipelines and analytics solutions.
• Support metadata management, data lineage, documentation, retention and governance requirements.
• Design solutions in accordance with GS1 information security, data privacy, access-control and responsible AI requirements.
• Implement role-based access controls, data classification and auditability appropriate to the sensitivity and intended use of the data.
• Identify and escalate data-quality, security, privacy, model-risk and governance concerns.

Engineering quality and production support

• Develop automated testing and validation for data pipelines, semantic models, reports and AI-enabled solutions.
• Use Git, source control, CI/CD and environment-management practices to support reliable deployment across development, test and production environments.
• Monitor critical solutions, resolve production incidents and troubleshoot pipeline failures, refresh errors, reporting issues, RAG pipeline errors and performance bottlenecks.
• Conduct root-cause analysis and implement preventative improvements.
• Maintain technical documentation, operational runbooks and recovery procedures, and contribute to release validation and business-continuity activities.

Technical leadership and collaboration

• Provide technical leadership on data architecture, solution design, engin…