Naresh Kotti

• Upgraded 5 manual loader data entry systems to automated data pipeline processing.
• Collaborate closely with business teams to understand their requirements and provide automated solutions for data processing and operational efficiency.
• Design and develop robust, scalable data pipelines using Python for efficient data processing.
• Deploy applications into AWS ECS Fargate environments, leveraging DevOps tools such as Git, Jenkins, Ansible, and CloudFormation.
• Develop RESTful services to support and integrate with front-end UI applications, ensuring seamless user interactions.
• Provide architectural designs and solutions for ETL applications, ensuring optimized data workflows.
• Conduct code reviews, offer constructive feedback, and ensure adherence to best coding practices.
• Guide and mentor peers on technical front, fostering collaborative and knowledge-sharing environment.
• Troubleshoot and resolve data quality issues raised by business stakeholders, ensuring data accuracy and consistency.
• Rewritten CDD code in GG to save 50K dollars per annum.
• Managing, Guiding 6 team members on regular work day.
• Increased customer satisfaction rates through diligent collaboration with cross-functional teams on feature development and bug resolution.
• Mentored junior engineers to strengthen their technical skills and improve overall team efficiency.
• Completed code debugging, software troubleshooting, root cause analysis, and program reviews.
• Collaborated on hiring, retention and promotion of junior engineers.
• Facilitated seamless system migrations with minimal downtime through meticulous planning and execution of migration strategies.

Designed and developed ETL pipelines using IBM DataStage, Python, and shell scripts.

• Ingested and integrated data from various source systems into databases and HDFS.
• Automated data processing with Python and PySpark, creating reusable scripts for efficient workflows.
• Developed and optimized SQL queries for both RDBMS (Oracle, SQL Server, DB2, PostgreSQL), and NoSQL databases (Cassandra, DynamoDB) to improve performance. While RDBMS systems followed traditional relational structures, Hive was leveraged for distributed data storage and querying, allowing SQL-like access to big data in Hadoop environment. Also, has experience in working with Graph DB, like Neo4j.
• Ensured data quality using SCD mechanisms and scripts to handle data issues.
• Collaborated with business teams to understand requirements and come up with solutions that best fits problem/requirement.
• Managed code integration, version control, and deployment through GitHub and DevOps tools.
• Provided support during testing phases, troubleshooting data, and deployment issues.
• Assisted in production deployments and data migration, ensuring smooth go-live processes.

• Designing and Developing ETL Jobs in IBM Infosphere DataStage tool.
• Creating integrated DataStage sequencer jobs to integrate multiple team members' work developed in parallel jobs.
• Participating in requirements calls with business teams, BA to understand requirements and gaining knowledge on systems.
• Working closely with quality team to process data on demand for testing and ensuring that the bugs are fixed in time to make product release as planned.
• Developing shell scripts to support Datastage jobs, used scripts to run DS jobs from scheduling tools like Gecs, Autosys.
• Rewriting PL/SQL objects (legacy code) in DataStage jobs.
• Shell scripts written to format error logs and send notifications with exceptions during job runs.