• Build and manage the semantic layer: Create the logical data layer, ensuring business rules are centralized and consistent across the company. • Governance and organization: Act as the guardian of data quality and standardization, structuring data so it is easily accessible before reaching analysts. • Requirements extraction: Interface directly with stakeholders to translate business needs into efficient dimensional models. • AI automation: Implement and promote the use of AI tools (such as Claude) to optimize development, documentation, and data processing. • Technical evolution: Maintain and evolve orchestration processes, unit tests, and versioning, applying engineering best practices to the analytics context.

• Developing and enhancing Dynamics 365 using Power Apps, configuration and JavaScript • Supporting opportunity, account, product and sales process functionality • Improving user experience and ensuring CRM capabilities align to agreed sales processes • Designing, building and optimising Power Automate workflows • Reducing manual processes such as: Account and opportunity creation, Renewals and contract extensions, Data cleansing and approvals • Supporting integrations across CRM, SharePoint and ETL tools • Supporting acquisitions, data migrations and onboarding into core sales platforms • Maintaining and improving product catalogue, pricing structures and sales templates • Ensuring clean, auditable data structures that support downstream reporting and analytics • Supporting SAP audit, commissions and financial reporting processes • Ensuring CRM data feeds meet requirements for forecasting, planning and finance teams • Providing second-line support for system issues and defects • Proactively improving system stability, performance and resilience • Supporting user adoption through fixes, enhancements and process improvements

• Participate in the transformation from traditional BI to an AI-augmented BI model. • Maintain and evolve data modeling and dashboards in Looker (LookML), ensuring operational continuity. • Build GenBI layers for automatic insight generation and conversational analytics. • Develop AI-driven data products, such as automated summaries and intelligent alerts. • Automate workflows, queries, and documentation using tools like Claude Code, Cursor, or similar. • Mentor the technical team, establishing new standards and analytics best practices. • Translate business challenges into high-impact analytical solutions for stakeholders and clients.

• Databricks ingestion and warehouse execution • Gold models, marts, and semantic layer delivery • Genie and AI-readiness of the data layer • Data quality, monitoring, and validation • Stakeholder enablement and warehouse adoption • Financial and business reporting support • Team collaboration and ways of working • Work closely with the BI Lead to turn roadmap items into concrete technical deliverables. • Provide effort estimates, implementation tradeoffs, and delivery risks. • Participate in code review, documentation, and shared standards for SQL, notebooks, jobs, and data assets. • Help strengthen self-service analytics by creating reusable assets and reducing manual workflows.