Mukeshreddy Ramgari

Analyzed 500K+ business records using SQL, Python (Pandas/NumPy), and Microsoft Excel to perform data cleansing, validation, and exploratory data analysis. Built and maintained 10+ interactive Power BI dashboards and 12+ automated reports to track KPIs and reduce manual reporting effort. Wrote and optimized 60+ SQL queries (CTEs, window functions, joins, subqueries, stored procedures) to extract/transform/validate data from PostgreSQL and SQL Server, reducing execution time. Configured 8+ data preparation and reporting workflows using Python and SQL, improving turnaround time while ensuring data consistency and 98% reporting accuracy through requirement gathering and root-cause analysis.

Worked on the Web Attack Simulator (WAS) platform using Python and customized Zed Attack Proxy (ZAP) modules to identify and simulate common web application vulnerabilities. Designed multithreaded/multiprocessing Python components to improve concurrent processing and reduce execution time for security scanning and automation workflows. Integrated Redis, MongoDB, Docker, Git, and Linux into development and deployment supporting 15+ production releases. Collaborated with Agile teams using JIRA to analyze, troubleshoot, and resolve 100+ defects, participating in planning, code reviews, and testing; maintained technical documentation and security/compliance records while delivering 10+ project milestones.

Developed and maintained backend modules using Python, Django, REST APIs, PostgreSQL, and SQL by implementing business logic and optimizing database queries to reduce API response time. Designed and built 18+ RESTful APIs integrating internal services and third-party platforms with JSON and JWT authentication, maintaining 99.5% API availability. Enhanced 10+ data processing and reporting workflows using Python, Pandas, and SQL for validation, transformation, and scheduled reporting—reducing manual effort by 55% and improving accuracy. Optimized PostgreSQL performance for 300K+ records using indexing and schema normalization, reducing query time by 30%, and supported releases through Git, Docker, CI/CD, unit testing, and Linux deployment practices.