Mrudul Agrawal

• Spearheaded enterprise-wide data infrastructure modernization on Azure, implementing cutting-edge solutions including Azure Data Factory, Fabric, and Databricks that reduced data processing time by 65% and increased analytical capabilities • Architected and deployed end-to-end cloud data ecosystem that transformed legacy systems into a streamlined, scalable solution meeting long-term business intelligence objectives. • Led an offshore team of Data Engineers, SQL Developers, and PySpark specialists, implementing Agile methodologies that increased delivery velocity by 40% • Developed comprehensive data mapping frameworks and ER diagrams using Lucid Chart, creating visual documentation that improved cross - functional understanding of complex data relationships • Established robust data governance protocols and security frameworks ensuring regulatory compliance while enabling greater data democratization • Orchestrated strategic integration between disparate systems, creating unified data models that provided executives with 360° visibility into business operations • Drove requirements gathering sessions with key stakeholders, translating business needs into technical specifications and architectural solutions • Implemented project management best practices using Wrike, resulting in 30% faster project completion and improved resource a location • Designed and implemented data quality monitoring systems that proactively identified and resolved data inconsistencies, increasing data reliability by 85% • Created forward-looking architectural roadmaps aligning technology investments with business growth strategies, ensuring scalability for business needs.

• Led the implementation of data warehousing solutions, proposing cloud technologies to facilitate scalable data storage and querying. • Automated SPC (Statistical Process Control) charts for industrialized bio-cement production, improving process efficiency and enabling real-time issue detection. • Developed SCADA and PLC integration solutions to streamline data collection from various manufacturing systems, ensuring seamless data flows. • Created data visualization dashboards and identified KPIs for data-driven decision-making, enhancing visibility into production processes. • Worked with MES (Manufacturing Execution System) integration, validating data integrity from process collection to storage in databases. • Implemented automation scripts using Python and SQL, significantly reducing manual intervention in production data monitoring. • Developed a data processing pipeline utilizing Jupyter Notebooks, which improved data transformation efficiency by 30%, and reduced processing time by 25% using Pandas and Apache Spark over a one-year period.

• Created Azure Data Factory pipelines using function apps and triggers for real-time data processing. • Developed and maintained robust data pipelines to collect, process, and transform healthcare data using Python. • Implemented data quality checks and ensured data integrity throughout the ETL (Extract, Transform, Load) process. • Identified and resolved data-related issues, contributing to the optimization of data processes, and improving overall system performance

• Extracted data using SQL from data sources and performed Exploratory Data Analysis (EDA). • Developed a Machine Learning model generating scores from data extracted. • Applied mathematical principles to optimize data processing algorithms, improving efficiency and accuracy in data transformations. • Collaborated with data scientists to understand statistical requirements and implemented data engineering solutions to support statistical analyses.