Rajesh M Email and Phone Number

• Led full-stack development of data migration and engineering solutions using Python frameworks (Django, Flask) integrated with Azure services like Azure Data Lake Storage, Azure Data Factory, and Azure Databricks, optimizing Engie's data management capabilities and ensuring smooth data pipeline execution from front-end to back-end.• Designed and optimized SQL queries within Snowflake and Big Query environments, building back-end logic to extract and process large datasets, enabling seamless data retrieval and integration with front-end applications for improved performance and real-time analytics.• Developed and deployed dynamic web applications using Python, JavaScript, and front-end technologies (HTML, CSS, React), ensuring responsive user interfaces and intuitive dashboards in Looker, empowering stakeholders with real-time data insights and enhanced decision-making.• Built and maintained robust back-end systems by implementing RESTful APIs, integrating cloud-based and on-premises databases (SQL, NoSQL), and utilizing Python for high-performance server-side logic, ensuring efficient data flow between front-end interfaces and back-end services.• Implemented monitoring and alerting systems using Azure Monitor and Python-based tools, enabling real-time tracking of web application health and data pipeline efficiency, with automated notifications and proactive error handling to optimize system reliability.• Managed CI/CD pipelines for full-stack Python applications, leveraging tools like Jenkins, Git, and Docker for seamless integration and deployment across multiple environments, ensuring high availability and smooth version control for continuous software improvements.• Collaborated with cross-functional teams to design, build, and scale full-stack web solutions, integrating advanced analytics with Python back-end services and interactive front-end interfaces, enhancing data-driven decision-making processes in renewable energy management.

• Assisted in developing Python and Java applications to enhance online banking functionalities; collaborated with cross-functional teams using Azure DevOps for project management and continuous integration.• Utilized Pandas, NumPy, and Apache Commons for managing and validating extensive banking datasets; implemented Azure Data Factory for automating data extraction and transformation processes.• Supported the maintenance and optimization of backend services using Django, Flask managed MySQL, PostgreSQL, and Azure SQL Database for improved API performance.• Automated data extraction and transformation with Azure Functions; created interactive visualizations using Matplotlib developed Power BI dashboards, leveraging Azure Analysis Services for advanced insights.• Assisted in creating and optimizing Power BI dashboards; supported data-driven decision-making; used Azure DevOps to streamline deployment processes and ensure efficient release cycles.• Contributed to the development of RESTful APIs in Python and Java; enhanced system efficiency and user interactions; deployed APIs on Azure App Services and managed them with Azure API Management

• Created a comprehensive dataset of 10,000 patient records using Pandas and NumPy, achieving an 85% accuracy rate in diabetes prediction models. Leveraged Google BigQuery for scalable data processing and analysis.• Conducted ETL processes to extract, transform, and load data using Google Cloud Dataflow and Python. Improved data quality for 90% of patient records by managing outliers and null values effectively, ensuring high-integrity data for analytics.• Resolved data quality issues and maintained system integrity using SQL and Google Cloud SQL. Implemented data validation and cleansing routines to support accurate and reliable reporting.• Developed insightful dashboards and reports using Google Data Studio and Google Looker. Employed advanced features including dynamic charts, graphs, KPIs, maps, and matrices, resulting in a 45% increase in data visualization and analysis efficiency.• Automated data workflows and reporting processes using Google Cloud Functions and Google Cloud Pub/Sub, reducing manual intervention and streamlining data updates and notifications.• Utilized Google AI Platform and BigQuery ML to build and deploy machine learning models for predictive analytics, enhancing the accuracy and depth of healthcare insights derived from patient data.• Worked with cross-functional teams to integrate data pipelines and reporting tools, leveraging Google Cloud Storage for efficient data storage and Google Cloud Composer for orchestrating complex workflows