Designed TDI 2.0 Framework using Java Spring Boot, implementing Spring BatchProcessing to ingest over 10,000 CSV records in a single upload. Developed an extractor microservice to download over 5,000 customer-requested records in CSV format, utilizing JOLT to flatten the payload and AWS Lambda to convert flattened JSON to CSV. Developed a unique ID generation library leveraging Redis to cache 1,000 pre-generated IDs per customer, with a built-in mechanism for duplicate ID detection. Collaborated with the Architect to redesign customer settings pages, implemented POC, coordinated with UI/UX teams, and managed API contracts. Consolidated tenant-related settings into a single searchable interface using Elasticsearch, tags, and hierarchical dependencies. Implemented a feature for customer Admins to create rules stored in MongoDB and Elasticsearch percolator, using ES functionality to match actions and send notifications, resulting in a 10x increase in requests and optimized system performance. Onboarded new entities into the notification system to receive personalized updates based on set criteria and rules. Identified slow MongoDB aggregate query. Analyzed execution stats to pinpoint inefficiencies. Optimized query, reducing document scans by 80% improving performance significantly. Collaborated with cross-functional teams to review solution documents, contacts, and PRs, while mentoring SDE1s and interns, supporting their professional growth and development. Revamped the error framework for TDI using Java Spring Boot and RabbitMQ, reducing SUP tickets tied to bulk upload file errors by 90% streamlining operations, and significantly enhancing user experience. Engineered a robust Rules Framework for Admin users using REST API and Spring Framework, enabling seamless feature toggling, reducing reliance on SRE teams, and cutting feature enablement tickets by 70%.