Reshma G

In thisrole developed multiple data pipelines to assess client credit card eligibility using an internal banking platform. Created monthly reports, pre-screened new customers, and generated preapproval letters. Responsibilities:  Followed SDLC methodology, participating in requirements gathering,system design, development,testing, deployment, and maintenance.  Utilized Azure Data Factory, T-SQL, Spark SQL, and Azure Data Lake Analyticsfor ETL processes, extracting, transforming, and loading data into Azure Data Storage services.  Worked with Azure Blob Storage and Azure Data Lake Storage to efficiently load data into Azure Synapse Analytics (Data Warehouse). Applied version control techniques with Git and best practicesfor source codemanagement, enabling seamless collaboration.  Designed and built complex ETL jobs using Azure Databricks and Azure SQLDatabase, transforming data through visual data flows and compute services.  Managed and optimized ETL/ELT processes by writing SQL and T-SQL scripts for data cleansing and transformation in Azure Synapse Analytics.  Developed and maintained Azure Data Factory pipelines for data extraction, transformation, and loading ETL) into Azure SQL databases based on business requirements.  Developed and optimized Spark applications using Scala, leveraging its performance benefitsfor big data processing. Automated the deployment and optimization of Python web applications using Azure DevOps CI/CD pipelines.  Managed structured, semi-structured, and unstructured data in Azure Databricks, optimizing performance.  Created Power BI dashboards and visualizations based on business and technical requirements.  Built data ingestion systemsto extract data from traditional RDBMS systems and store itin NoSQL databases (MongoDB) for flexible data management. Designed ETL pipelines for Snowflake, leveraging its powerful SQL capabilities for financial reporting.

In this project, we use the SQL Server Integration Services (SSIS) Extract, Transform, and Load (ETL) procedure to import the raw data to the data warehouse. The import connection to that warehouse database. Data was cleaned up,and after that, it was changed and sent to the Power BI tool via the Power Query editor. We have started the visualization process based on the client's requirements. Ten different standard charts were used in this project. After finishing this report, we submitted it to the review team for evaluation. Responsibilities:  Worked on how to handle security according to needs when creating reports, charts, and dashboards using Power BI Desktop.  Used Power Query, transform, define, and browse the data from the data source and execute merge and append queries.  Used T-SQL constructs for data querying, such as joins, sub queries, derived tables, and views.  Worked on many DAX functions have been implemented for various fact calculations to facilitate effective data display in Power BI.  Used detail level summary report sets, groups, hierarchies, backdrop images, maps, trend lines, computations, advanced analytical activities, and KPI dashboards.  Worked on Power BI desktop was used to create Power BI Reports utilising the Tabular SSAS models as the source data and publish reports to service.  Worked to validate static and dynamic data for data validation, simultaneous SQL queries with join clauses (inner, left, and right) were developed and examined in Tableau desktop.  Created sophisticated T SQL / P SQL queries for SSRS Report join conditions, unions, aggregates, and expressions.  Several data sets on Azure were successfully created during the upgrade of SQL Server 2008, 2012, and 2014 to SQL Server 2016.  SSAS implemented and created Star and/or Snowflake schemas for creating relational databases and data warehouses to meet business and report requirements.