• Develop, maintain and optimize robust large-scale data ingestion and transformation pipelines, both batch and streaming. • Build end-to-end data products such as dashboards, automated alerts, internal tools and self-service frameworks for Product and Ops teams. • Integrate data from multiple platforms and tools, such as Azure, GitHub, Mixpanel, ADP, Notion, Google Sheets and various APIs, creating a unified, strategic view of the operation. • Design reliable, scalable and governed data models, ensuring data quality, traceability and consistency. • Lead refactoring initiatives and the organization of legacy environments, driving continuous improvements in architecture and performance. • Implement analytics engineering best practices, including version control, automated testing, observability and monitoring. • Automate operational and governance workflows using integrations, APIs, alerts and data-driven solutions. • Collaborate with stakeholders across Product, Engineering, Operations and leadership to translate strategic needs into scalable solutions. • Act as a technical reference for decisions related to the architecture and evolution of the data ecosystem. • Foster a data-driven culture, enabling teams to consume reliable insights autonomously.

• Transform curated datasets into BI‑ready semantic models, dashboards, and analytics marts. • Translate business requirements into governed data models and well‑defined metrics. • Define and document metric logic, data lineage, and semantic layer standards. • Maintain and support dashboards and analytics products. • Design, develop, and deploy data and AI solutions on the Databricks platform. • Build and maintain ETL and ELT pipelines. • Collaborate with data engineers, data scientists, and business stakeholders to deliver analytics solutions.

• 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