Deepa Shanmugam

Persistent professional equipped with 10+ years of comprehensive experience in IT industry. Provides strong technical direction across multiple projects. Responsible and articulate individual with strengths in solutions optimization, mobile and cloud solutions and global project management.

• Recognized with two awards for outstanding performance
• Played an important role in enhancing the auto approval process from 7 days to 1 day by introducing Kafka pipeline

• CPM Event Processor - The implementation of Pub-Sub capability for consumers has been achieved by leveraging Kafka's functionalities, facilitating the construction of an event pipeline for transferring data from the source to Kafka topics. The technologies utilized include Confluent Kafka, JDBC Source Connectors, Core Java (Apache Kafka Stream), and SQL.
• BPI-NRT - The project's aim is to serve as the primary data repository, delivering records in near real-time. It utilizes technologies such as Spring Boot, Kafka, and ELK to achieve this objective efficiently.
• BCIM - The scope of projects is to expose the BCIM details to External Team using Kafka pipeline.
• AA Enhancement - Implemented the Kafka pipeline to streamline the auto approval process, slashing the turnaround time from 7 days to a single day. Leveraged technologies including Apache Kafka, SQL, and MSSQL Server for this optimization.
• CDC Enhancement - By implementing a Kafka Pipeline into the existing projects and leveraging technologies like Spring Boot and Kafka, significant performance enhancements were achieved, notably in reducing queue latency and Oracle DB CPU performance.
• Outage Notifications - A centralized system was developed to manage notifications of outage declarations from multiple systems and disseminate them to customers via email and SMS. Utilizing Kafka, Spring Boot, and ELK technologies, the system efficiently collects and distributes outage alerts, ensuring timely communication with customers.
• MQ Removal Contact - The scope of this project encompasses retrieving contact data from the Master Data Management (MDM) system, performing data enrichment, and subsequently publishing the finalized data to an Elasticsearch index. This was accomplished using technologies such as Spring Boot, Kafka, and the ELK stack.
• Geosite Management System - The Site Management data is centralized in CPM and transmitted to the ELK Index through a Spring Boot API. Subsequently, retrieval of data from the ELK Index is facilitated using key values via API. The technologies employed include ELK, Ruby, SQL, and Spring Boot.
• Monitoring - Using JMX Exporter persist the logs via Prometheus and created visual dashboard in Grafana for Kafka, Logstash scripts and spring boot applications.