• Excellent Knowledge in RDBMS Concepts and Data Modeling using Star and Snowflake Schemas. Hands-on experience on Data warehouse Star Schema Modeling, Snow-Flake Modeling, Kimball Model, FACT & Dimension Tables, SCDs and Logical Data Modeling.• Proficient in writing T� SQL Statements, Complex Stored Procedures, Dynamic SQL queries, Batches, Scripts, Functions, Triggers, Views, Cursors (prefer Temp Tables) and Query Optimization.• Responsible for performance tuning of stored procedures, Database Tables using Table Partitioning, SQL Profiler and Database tuning wizard, Looking at Execution Plans• Skilled in High Level Design of ETL Packages for integrating data from heterogeneous sources Excel, CSV, SQL or other DBs, flat file, xml, Parquet, Json data.• Worked on Notification services in setting up the Scheduled jobs and alerts.• Use Azure Data Factory to create linked services to your data sources. Configure data extraction pipelines to periodically pull data from these sources.• Utilize Azure Integration Runtime for seamless connectivity to on-premises and cloud sources.• Use Data Flows in Azure Data Factory for data transformation tasks, including data cleansing, aggregation, and enrichment.• For complex transformations and large-scale data processing, leverage Azure Databricks to create and run Spark jobs.• Implement transformation logic using SQL scripts and U-SQL within Azure Synapse Analytics for scalable and efficient data processing.• Utilize PolyBase in Azure Synapse Analytics for efficient loading of large volumes of data from various sources.• Configured Resource groups for proper manageability and access.• Created several Azure functions written in python for maximum Data transformation and loading• Home grown ETL framework for complete Data transfer/transformation, Incremental and batch wise loads with audit logging, fully automated, restartability and data plumbing from various types of Sources

• Serve as Services Solution Architect responsible for translating the client's business requirements into specific systems, applications or process designs with interlocked financial modeling for very large complex technical solutions.• Work with a team of DevOps/SecOps Engineers to architect AWS solutions with the responsibility to ensure the customermaintains an overall cohesive integration with partner systems, and solution• Support and advise on application assessment, migration and modernization.• Design cloud infrastructure, virtual private cloud, virtual private network, relational database services, auto scaling, and computing resources to meet customers' requirements.• Maintain an understanding of advancing cloud capabilities, IT and business needs, while identifying and proposing cost andfunctional efficiency options for the customer's collaborative decisions and actions.• Worked on Multiple on prem to Cloud Migration Projects.• Provided Birds eye level Architecture documentations for several Cloud based ETLs and Provided step-by-step migration and ETL plans.• Standardized Development using Git along with providing initial installation and usage guidance via UI based tools and command line code.• Hands on with Coding and setting up EC2 instances, deploying Kinesis streams, setting up S3 Buckets.• Data governance with proper documentation techniques, with following best business practices, naming conventions.• Good understanding of granularity of data in S3. Excellent in implementation and using Redshift or Snowflake as the Cloud Datawarehouse and integrating with PowerBI or Tableau

• Actively interacted with end users to gather business requirements, system specifications, and enhancements.• Performed System Study and Requirements Analysis, prepared Data Flow Diagrams, Entity Relationship Diagrams, Data Diagrams, and Table Structures.• Actively involved in 3 phases of the Automated ETL homegrown framework, making troubleshooting, re-startability, and near real-time data load an easy task.• Designed, implemented, and maintained all AWS infrastructure and services within a managed service environment.• Performed data migration from on-premise environments into AWS services.• Led development team in designing/migrating AWS cloud-based solutions and performed data migration from on-premise environments into AWS services.• Worked with distributed teams to design and develop frameworks, solution accelerators, proof of concepts, and external customer-facing products.• Designed both technically compliant and secure cloud solutions, as well as value-based, on-demand services to facilitate the effective transition and migration of projects and programs into a unique and adaptive cloud environment.• Created a lift-and-shift process model, clearly defining the individual steps of the lift-and-shift process.• Dealt with ETL, transformation, and optimization of terabytes of data.

Drove architectural plans for future data storage, reporting, and analytic solutions, achieving a 20% increase in dataretrieval efficiency.•Automated ETL processes for over billions of rows of data, reducing manual labor by 45 hours per month andincreasing processing speed by 30%.• Collaborated with Senior Architects to develop automated data pipelines that efficiently processed over 100 milliondata points, enhancing data flow by 25%.• Implemented multiple projects on Microsoft Azure, setting up Azure Data Factory pipelines that improved dataintegration efficiency by 40% and utilized Azure Databricks to accelerate big data analytics processing by 50%.• Managed Azure Synapse Analytics to scale data warehousing solutions, which improved data access and analysiscapabilities by 60%, directly impacting strategic decision-making.• Trained in troubleshooting ETL Processes and Data pipelines, reducing downtime by 40% and enhancing systemreliability.• Engaged with clients to convert business requirements into visualizations using Tableau, reducing weekly manual effortby 15 hours and improving data-driven decision-making by 35%.• Ingested data from various sources using SQL to build views for BI tools like Tableau and AWS Quicksight,increasing the accessibility and usability of integrated data by 45%.• Refined SQL queries for large and complex datasets, boosting query performance and data retrieval efficiency by 50%.Applied expert knowledge in data warehousing concepts like Star Schema and SQL/NoSQL databases, facilitatingscalable and efficient data management solutions that supported a 30% improvement in data system performance.