Data Engineering plays an important role in the Data Science and Analytics industry to ensure that the data availability is seamless, efficient and accurate for further analysis. Rapid evolution of data engineering facilitates the need for robust and standardized approaches of system integration in data engineering. With increase demands for scalability, reliability, efficiency and inconsistent methodologies can lead to fragmented solutions, operational inefficiencies and high maintenance cost, thus standardization of system integrated data engineering solution helps to mitigate these challenges achieve the timely and effective data consumption, storage and availability. Also, managing these system integration solutions becomes challenging if business runs under different industries, and different platforms. Hence it is very important for data engineering architects to standardize the system integrated solutions when it comes to data engineering project implementation. This article explains feasibility of standardizing the system integrated data engineering architecture and proposing a framework that emphasizes uniform practices in areas such as data ingestion, transformation, storage, and retrieval across diverse platforms by conducting a case study of leading Global Online Retail Company. As more data sources and more business problems are involved in data engineering, the architecture becomes more and more complex to manage. The core and integral part of data engineering is to ensure ingestion and storage of data

AWS (Amazon Web Services) System Integration ETL pipelines Cloud Native Solution Data Engineering.