The growing availability of data offers significant opportunities to improve data-driven decision-making in complex organizational contexts. In higher education, effectively integrating and analyzing heterogeneous data sources is essential for evaluating educational programs, supporting governance processes, and enabling evidence-based planning. However, institutional and external data are often fragmented across multiple systems and formats and subject to access and governance constraints, making systematic analysis difficult without dedicated data engineering solutions. This thesis presents the design and implementation of a containerized, end-to-end data pipeline for data integration, enrichment, and visualization to support analytical activities in academic environments. The proposed methodology is based on a modular architecture that combines data ingestion from heterogeneous sources, transformation and harmonization workflows, structured storage, and interactive dashboards.