Data engineering is a pivotal field for building and reengineering the data of organizations. It allows the organizations to receive and analyze the data based on the amount of data. The data engineering field is subject to rapid evaluation with the emergence of new technology and tools for the improvement of data security, transparency, and storing data. Data engineers use these new tools and techniques to enhance the data processing and analysis for the companies. However, data transparency, bias in decision-making, and utilizing more power are the challenges in data engineering. Various policies, regulations, and laws are created that are based on data security. This chapter aims to explore the technology, tools, and techniques that help to provide accurate predictions, enhance data quality, and data analysis. The proposed techniques analyzed in the research are green store data, data pipeline that is energy-efficient, and cloud-based architecture to minimize the power that enables achieving sustainable goals and enhancing the operational efficiency. The integration of data engineering with sustainable practices increases business success and reduces the impact of environmental stress. The increasing demand for AI and sustainable operational data requires the implementation of data governance, which can manage the vast amount of data using AI tools in future research.