Requirement gathering from business users, Problems Identification and Solution Architecture Proposal, Discussion with team to list down various implementation approaches to understand the complexity, impacts and challenges in Data Engineering Solutions, Breakdown requirements into smaller technical tasks, highlights risks and dependencies, prioritize the Jira Board stories, scoping and estimation,   Develop prototypes, perform proof of concepts (POC), finalize the architecture diagrams and design customization, Develop comprehensive ETL Pipelines using Data Engineering tools and technologies such as Mongo DB, Python, AWS Cloud Services, complex Oracle SQL Queries and Unix scripting. Extract the data from various heterogenous data sources, data cleaning, data staging, data loading, storage into cloud storage given by Amazon Web Services, and extraction for data analysis and business intelligence. Develop automation scripts and modules to reduce manual intervention for reusable ETL components and deployments, Improve complex SQL Queries, Mappings, Python Programs, Database Designs and Unix processes,   Perform unit testing, extract baseline, prepare run book, do technical shakeout in Production environment, and provide rollback plan for any release issues,   Documentation of the in-depth design, architecture, process flow and programming logic for future enhancement reference, and smooth resource onboarding in the project, Prepare knowledge articles with recovery and job execution steps for Level2 (L2) Support