In the era of data-driven decision-making, organizations face significant challenges in scaling Agile practices to complex data programmes. This paper explores the application of Lean-Agile principles in the context of a large-scale data initiative within data-driven company. Drawing from real-world experience as a Release Train Engineer, Chief Scrum Master and Agile / Teams Coach, it examines how Agile frameworks, particularly the Scaled Agile Framework (SAFe), can be adapted to address the unique constraints of data programmes—such as data dependencies, governance, security, and cross-functional collaboration. The paper discusses key implementation strategies, including incremental-iterative delivery, value stream alignment, and Build-in-Quality practices in enabling continuous integration and deployment of data products. It highlights common challenges, such as managing technical debt in data ecosystems, fostering Agile mindsets in traditionally structured data teams, and balancing speed with regulatory compliance. Lessons learned from multiple Agile increments provide insights into optimizing team performance, improving stakeholder engagement, and enhancing business value delivery. By synthesizing practical experience with SAFe Lean-Agile theory, this study provides actionable recommendations for Agile practitioners, data leaders, and organizations embarking on their own Agile data transformations. Ultimately, it contributes to the ongoing discourse on integrating Agile methodologies within data-intensive environments, bridging the gap between theory and practical execution.