1. ETL Pipeline Development: Built scalable ETL pipelines with Azure Data Factory, AWS Glue, Alteryx, and AWS Lambda, leveraging Azure Functions for automated data processing to handle high-volume financial data efficiently.2. Azure SQL Data Warehouse: Designed and managed an Azure SQL Data Warehouse and Snowflake with enhanced workflow orchestration, optimized partitioning, indexing, and query tuning for fast access to analytics-ready data across departments.3. Data Transformation & Predictive Analytics: Used Azure Databricks with Python, Pandas, NumPy, PySpark, and integrated RESTful/SOAP APIs for complex data transformations and predictive modeling on market risks and trends.4. Workflow Automation: Automated data workflows using Azure Logic Apps, AWS Step Functions, and Terraform, ensuring seamless data ingestion, transformation, and redundancy with error handling.5. Performance Monitoring: Deployed Azure Monitor and Application Insights for proactive pipeline monitoring, setting custom alerts and dashboards for minimal downtime and rapid troubleshooting.6. Real-Time Dashboards: Developed Power BI and Tableau dashboards with automated data refresh and filtering, providing portfolio managers with interactive, real-time insights on investment metrics7.. Working in an Agile Environment: Participated actively in sprint planning, stand-ups, retrospectives, and iterative project delivery using JIRA.Skills: MySQL, Business Analysis, Python, Business Process, Stakeholder Management, Communication, Creative Problem Solving, Azure Data Factory, Azure Databricks, Azure DevOps Server, Azure Functions, Azure Logic Apps, Azure Data Studio, Microsoft Azure Machine Learning, AWS Glue, AWS Lambda, Terraform, GitHub, Tableau, Change Control Management, JIRA, SDLC, RESTful/SOAP APIs, Snowflake, AWS Step Functions.

Real-Time ETL Pipeline Development: Designed real-time ETL pipelines using Google Cloud Dataflow, Apache Beam, AWS Glue, Alteryx, and AWS Lambda for processing high-frequency IoT data from healthcare equipment. Data Storage and Analysis: Centralized data storage in Google BigQuery, Snowflake, and AWS S3, optimizing with advanced SQL and partitioned tables for faster query performance. Predictive Modeling for Maintenance: Built predictive models on Google AI Platform using Python, TensorFlow, PySpark, and RESTful/SOAP APIs to forecast equipment failures. Automated Processing: Automated ETL workflows with Google Cloud Functions, AWS Step Functions, Apache Airflow, and Terraform for real-time data transformations. Performance Monitoring: Implemented Google Stackdriver, AWS CloudWatch, and Tableau for pipeline monitoring, custom alerts, and key metric dashboards. Real-Time Dashboards: Created dashboards in Google Data Studio and Tableau, integrating IoT and predictive data with dynamic filters. Data Security & Compliance: Enforced security with Google Cloud IAM, AWS IAM, Cloud Key Management, VPC Service Controls, and Change Control Management. Workflow Orchestration: Used Apache Airflow, AWS Step Functions, and Alteryx for scheduling and managing data workflows. Skills: Google Cloud Dataflow, Apache Beam, AWS Glue, Alteryx, AWS Lambda, AWS S3, Snowflake, BigQuery, Google AI Platform, Apache Airflow, Google Cloud Functions, Google Stackdriver, AWS CloudWatch, Tableau, Google Data Studio, Terraform, RESTful/SOAP APIs, Python, TensorFlow, PySpark, VPC Service Controls, Google Cloud IAM, AWS IAM, Change Control Management.

• Designed and implemented ETL processes using Azure Data Factory to integrate data from various mobile application platforms.• Created and maintained data pipelines for real-time analytics, leveraging Azure Stream Analytics to process user engagement data.• Developed interactive dashboards in Power BI to provide insights into app performance, user behavior, and engagement metrics.• Managed structured data in Azure SQL Database, ensuring data quality and integrity for analytics purposes.• Automated reporting workflows using Azure Logic Apps to streamline data updates and distribution of insights to stakeholders.• Collaborated with cross-functional teams to gather requirements and provide data-driven recommendations for application enhancements.

• Developed data pipelines with AWS Glue to process customer feedback and product usage data for analytical reporting.• Managed data storage in Amazon S3, ensuring data accessibility and security for analytics teams.• Conducted data transformation and analysis using AWS Lambda functions and Python to derive actionable insights from raw data.• Created real-time dashboards in Amazon QuickSight to visualize key performance indicators and trends in user feedback.• Automated data processing workflows with AWS Step Functions to enhance the efficiency of data ingestion and reporting.• Collaborated with product managers and UX teams to identify data needs and support strategic decision-making with analytical insights.