Addressed complex datasets from deep Space Exploration projects, utilizing Python, SQL, and Tableau tocatalyze insights that streamlined educational content delivery, resulting in a 20% increase in studentengagement in astronomy modules.Managed data collection and research for FM Radio receiver and Digital clock building workshops,optimizing learning experiences with embedded systems like Arduino and Raspberry Pi.Partnered with engineers and educators to transform data into impactful insights, supporting innovativeproject outcomes in collaboration with Synergistics India.Developed and maintained dashboards to track project milestones and performance, boosting operational efficiency by 30% across workshops while integrating artificial intelligence into analysis workflows.

In my previous role as a workforce reporting professional, I successfully managed and analyzed workforce data, providing valuable insights for strategic decision-making. I collected, organized, and maintained employee data, performance metrics, and training information. Utilizing data analysis techniques, I extracted meaningful trends and insights to track key metrics and benchmarks.To effectively communicate these insights, I created comprehensive reports, interactive dashboards, and visualizations, presenting the workforce data in a clear and concise manner. These reports covered areas such as employee turnover rates, performance benchmarks, diversity metrics, and workforce planning forecasts, enabling informed decision-making at different organizational levels.In summary, my experience in the workforce reporting role involved proficient data management, analysis, compliance, collaboration, and continuous improvement.

Worked on Informatica Power Center Client application with the tools to manage the repository and to design mappings, mapplets, and sessions to load the dataUnderstood the basic concepts of Extract, Transform, Load (ETL), got familiar with data warehousing principles, data integration processes, and overall ETL workflow.Proficiency in data profiling techniques to analyze and understand source data structures, relationships, and quality, using this skill helped identifying data quality issues and designed appropriate transformation logic.