Senior BI Full Stack Developer with Power BI and Data Engineering, Global Development Data Platform

Client Our client is a well-known British Asset management company. Project overview You will contribute to a data and analytics platform that supports strategic decision making within international development initiatives. The product focuses on transforming diverse data sources into reliable insights and analytical models that help guide sustainable economic programs. Team You will join a cross functional team of data engineers, BI developers, data modellers, and analytics specialists. The team works collaboratively, follows structured delivery practices, and partners closely with business stakeholders to refine requirements and improve data quality across the platform. Technology stack Power BIEnterprise data warehouseSQLData engineering toolsData modelling practicesETL pipelinesVersion control toolsCloud based data services Responsibilities • Build and maintain Power BI solutions including data models, visualisation layers, and reporting logic • Design and develop data pipelines that move and transform data into the enterprise data warehouse • Apply data modelling practices to support analytical tasks and reporting structures • Collaborate with analysts and data owners to clarify requirements and align on business logic • Improve BI architecture by refining data flows and optimising performance • Validate data quality and ensure consistency across source systems and analytical layers • Document data structures, workflows, and modelling decisions in a clear and accessible way Requirements • Experience building data warehouses and semantic models • Experience developing BI solutions using Power BI • Experience with SQL • Ability to collaborate with technical and non technical stakeholders • Ability to work independently within a structured delivery process Nice to have • Experience working in teams that use Scrum or Kanban practices • Experience designing or maintaining ETL processes • Experience supporting environments with complex or high volume datasets • Experience in the financial domain