Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The integration of Artificial Intelligence, Data Lakes, and Cloud Architecture represents a transformative force in enterprise computing, creating unprecedented demand for professionals with cross-domain expertise. This technological convergence enables organizations to extract actionable insights from vast quantities of data while significantly improving operational efficiency. The adoption of cloud-native lakehouse architectures has revolutionized enterprise data infrastructure, combining flexible storage capabilities with performance optimizations that enable a unified platform for both analytical and AI workloads. Professionals mastering this technological intersection command substantial compensation premiums, yet organizations face significant challenges in talent acquisition. This article presents an evidence-based framework for career development, outlining core competency domains and providing a structured progression pathway. The framework identifies three essential skill pillars: cloud platform expertise, data engineering proficiency, and AI/ML implementation capabilities. Career development follows a progressive trajectory from foundational skills through intermediate technologies to advanced enterprise-scale capabilities. Practical implementation through project-based learning emerges as the critical differentiator, with hands-on experience substantially outweighing theoretical knowledge. Strategic approaches to portfolio development, certification, and professional visibility further accelerate career advancement in this rapidly evolving field.