Mallikarjun Reddy Ramasani

Mallikarjun Reddy Ramasani has built his career in the evolving field of business intelligence engineering, where technical depth must be matched by the ability to turn data platforms into practical decision systems. Over the course of a decade working with major financial institutions, telecommunications organizations, and technology-focused enterprises, he has consistently focused on extending the capabilities of BI platforms beyond conventional reporting. His work spans enterprise data visualization, ETL design, cloud-enabled infrastructure, machine learning integration, CI/CD automation, and performance-oriented platform engineering, reflecting a professional profile shaped by both breadth and technical specialization.

At the center of Ramasani’s expertise is a strong command of the leading business intelligence ecosystem, including Qlik Sense, QlikView, Tableau, and Power BI, along with advanced capabilities in set analysis, QVD creation, data modeling, ETL process design, and scripting. He has also expanded this core BI foundation into machine learning and application integration through Python, using libraries and frameworks such as pandas, numpy, Django, Flask, scikit-learn, and TensorFlow. His work with Tableau TabPy to embed live sentiment analysis and machine learning outputs directly into dashboards illustrates a professional orientation toward making analytics systems more predictive, interactive, and operationally valuable. On the infrastructure side, his experience includes Red Hat Linux, AWS EC2, VPC, VMware, Docker, Jenkins-based CI/CD pipelines, and Maven artifact management, complemented by a Microsoft Azure Developer Associate certification.

At Barclays, Ramasani carried out some of his most advanced work at the intersection of BI architecture and applied machine learning. He developed sophisticated Qlik Sense data models and dashboards using set analysis, QVD creation, and ETL processes to deliver actionable intelligence across the organization. He also designed Tableau dashboards that emphasized strong data visualization and storytelling practices to support business decision-making. By implementing Python for data analysis, automation, and application development, he improved dashboard performance and data refresh efficiency by 40%. His work also included building and deploying machine learning models using scikit-learn and TensorFlow for predictive analytics, as well as automating report generation and multi-format distribution through NPrinting. This body of work demonstrates not only proficiency in dashboard engineering, but also the capacity to transform BI environments into more intelligent and automated enterprise systems.

At AT&T, through Technosoft Group, Ramasani addressed the challenge of enterprise-scale infrastructure monitoring. He developed a Qlik Sense dashboard that enabled server administration teams to monitor enterprise-wide server health from a unified interface. This required integrating metrics from multiple systems into a coherent operational view and applying full lifecycle BI skills across data modeling, testing, design, development, and implementation. He also modernized the analytics environment by migrating legacy reports from Business Objects, SSRS, Eagle, and other transaction tools into Qlik Sense. Governance responsibilities included token assignment and custom security role configuration, while Python integration helped accelerate analytics delivery by 20%. His use of sentiment analysis through Tableau TabPy further expanded the system from static reporting into real-time intelligence.

His work at Broadridge, through Streams INC, and at Freddie Mac, through Career Soft Solutions, shows another important side of his expertise: building reliable and maintainable BI environments on secure infrastructure foundations. At Broadridge, he engineered QlikView and Qlik Sense dashboards using advanced data modeling techniques including star schema design, loop resolution, synthetic key elimination, set analysis, and aggregation functions. He also implemented incremental load strategies for insert, update, and delete operations, and improved maintainability through QVD-based script externalization. In Red Hat Linux enterprise environments, he supported package management, systems integration automation, and infrastructure performance monitoring. At Freddie Mac, he designed and optimized QlikView dashboards and data models, while also implementing end-to-end CI/CD pipelines that automated code integration, artifact deployment, and package management. His use of AWS, VMware, Docker, and Linux infrastructure in these roles reflects a BI engineer comfortable operating across both analytics and DevOps-adjacent responsibilities.

Earlier in his career, Ramasani developed a strong grounding in quality engineering, automation testing, and validation, which remains visible in the rigor of his later platform work. At Freddie Mac through Collabera, Niftek, and Trinity Technosoft, he worked on agile testing practices, acceptance test automation with Selenium and Cucumber, API automation using REST Assured, and SQL-based data validation across Oracle supporting tables and cubes. He also implemented AngularJS automation using Protractor and Jenkins-based continuous integration. At IBM, through Nic Info Tek, he enhanced testing practices with Java design patterns and implemented Spring Security with role-based access control. This quality-centric foundation is significant because it helps explain the reliability, stability, and governance orientation that characterize his later BI and infrastructure contributions.

At SUN-IT Solutions, he further established this discipline by designing more than 150 manual and automated test cases, reducing post-release defects by 35% and improving release confidence across QA cycles. He also streamlined regression and smoke testing using VBScript in QTP and Core Java automation, cutting testing time by 25% per sprint. These early contributions reveal that his later success in business intelligence and analytics engineering was built not only on technical ambition, but on an enduring commitment to validation, trustworthiness, and execution quality.

Taken together, Ramasani’s career reflects a technologist who has consistently pushed BI platforms beyond descriptive reporting into richer, more integrated decision-support systems. He has combined analytics engineering, machine learning integration, infrastructure automation, cloud deployment, and quality discipline to build enterprise systems that are more scalable, more intelligent, and more operationally dependable. His work has improved performance, accelerated insight delivery, modernized legacy reporting ecosystems, and strengthened the technical foundations on which business intelligence environments depend.

For IICSPA Fellowship consideration, Mallikarjun Reddy Ramasani presents a strong profile defined by sustained technical contribution, cross-domain engineering breadth, measurable enterprise impact, and a clear record of advancing business intelligence systems through innovation and disciplined execution. His professional journey reflects the level of distinction, maturity, and practical influence expected of a fellowship-level candidate.