Hasitha Vallamalla

• Responsible for developing ETL pipelines to meet business use cases by using data flows, Azure Data Factory (ADF), Data Lake and Azure Datawarehouse
• Created Pipelines in Azure Data Factory using Linked Services/Datasets/Pipelines to Extract, Transform and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse
• Used complex data transformations and manipulations on business use cases/ requirements with Data flows, Databricks
• Created Azure data factory Pipelines to pull data from Blob Storage account to Azure SQL Data warehouse, Azure SQL Data base and Azure Data Lake storage as a full/Incremental load based on frequency at which the data arrives in to corresponding tables/folders.
• Perform data extraction, aggregation, and quality checks from multiple sources
• Involved in Designing the Data Warehouse and creating Fact and Dimension tables with Star Schema and Snowflake Schema.
• Implemented Normalization rules in database development and maintaining Referential Integrity by using Primary Keys and foreign keys
• Utilize Databricks Delta Lake storage layer to create versioned Apache Parquet (delta) files with transaction log and audit history
• Used Spark SQL for reading data from external sources and processes the data using Python computation framework.
• Performed Power BI Desktop Data modeling, which cleans, transforms, mash up Data from multiple sources.
• Involved in creating Filters, quick filters, table calculations, calculated measures, and parameters in Power BI.

Responsible for developing ETL pipelines to meet business use cases by using data flows, Azure Data Factory (ADF), Data Lake and Azure Datawarehouse
Created Pipelines in Azure Data Factory using Linked Services/Datasets/Pipelines to Extract, Transform and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse
Used complex data transformations and manipulations on business use cases/ requirements with Data flows, Databricks
Created Azure data factory Pipelines to pull data from Blob Storage account to Azure SQL Data warehouse, Azure SQL Data base and Azure Data Lake storage as a full/Incremental load based on frequency at which the data arrives in to corresponding tables/folders
Perform data extraction, aggregation, and quality checks from multiple sources
Involved in Designing the Data Warehouse and creating Fact and Dimension tables with Star Schema and Snowflake Schema
Implemented Normalization rules in database development and maintaining Referential Integrity by using Primary Keys and foreign keys
Utilize Databricks Delta Lake storage layer to create versioned Apache Parquet (delta) files with transaction log and audit history
Used Spark SQL for reading data from external sources and processes the data using Python computation framework
Performed Power BI Desktop Data modeling, which cleans, transforms, mash up Data from multiple sources
Involved in creating Filters, quick filters, table calculations, calculated measures, and parameters in Power BI

Involved in data warehouse implementation on Azure Synapse using Synapse pipelines, notebooks, SQL Serverless, and dedicated SQL Pool
Leveraged Azure Cloud resources – Azure Data Lake Storage Gen2, Azure Data Factory, and Azures Data warehouse to build and operate a centralized cross-functional Data analytics platform
Developed Azure Synapse and Data bricks notebooks to Join, filter, pre-aggregate, and process the files stored in Azure data lake storage using PySpark
Created reusable pipelines in Data Factory to extract, transform and load data into Azure SQL DB and SQL Data warehouse
Created logical and physical data models of existing and new Data solutions for developers and other users to understand data structure within Azure Synapse dedicated SQL pool
Created Azure Synapse Pipelines using Linked Services/Datasets/Pipeline/ to Extract, Transform, and load data from different sources like Azure SQL, Blob storage, Azure SQL Data warehouse, write-back tool and backwards
Use various types of activities: data movement activities, transformations, and control activities; Copy data, Data flow, Get Metadata, Lookup, Stored procedure, Execute Pipeline
Developed an automated process in Azure cloud which can ingest data daily from web service and load into Azure SQL DB
Developed complex SQL queries using stored procedures, common table expressions (CTEs), temporary table to support Power BI reports
Implemented complex business logic through T - SQL stored procedures, Functions, Views, and advance query concepts
Involved in logical modeling, physical database design, data sourcing and data transformation, data loading, SQL, and performance tuning
Setup and maintain the Azure SQL Database, Azure Analysis Service, Azure SQL Data warehouse, Azure Data Factory, Azure SQL Data warehouse
Used Azure Logic Apps to develop workflows which can send alerts/notifications on different jobs in Azure
Used Azure DevOps to build and release different versions of code in different environments
Created External tables in Azure SQL Database for data visualization and reporting purpose
Recreating existing application logic and functionality in the Azure Data Lake, Data Factory, SQL Database and SQL Datawarehouse environment
Build Complex distributed systems involving huge amounts of data handling, collecting metrics building data pipeline, and Analytics

Actively participated in client and stakeholder meetings as a senior BI developer and helped Architect to design technical solutions for business requirements of end users
Developed and orchestrated ADF pipeline activities for parallel and sequential data load using paraments and variables
Scheduled ADF pipelines based on triggers such as Schedule, Tumbling window, Event-based
Implemented complete CI/CD process in new and existing ADF environment while making credentials secure in Azure Key Vault, and deployed production ready ADF resources using Azure pipeline releases
Developed and implemented proactive monitoring solution for slow running ADF pipelines
Created Azure Logic Apps for various monitoring and automated purpose
Created alert to monitor Azure SQL Database and scaled up/down depending on the usage
Created complex and efficient dynamic SQL Queries, sub-queries, and complex joins for generating Complex Stored Procedures, Triggers, User-defined Functions, Indexes, and Views
Design and implement database solutions in Azure SQL Data Warehouse, Azure SQL
Performed ETL operation using Data factory and Databricks and loaded the data into Azure SQL DW
Designed, reviewed, and created primary objects such as views, indexes based on logical design models, user requirements and physical constraints
Worked with stored procedures for data set results for use in Reporting Services to reduce report complexity and to optimize the run time. Created Complex ETL Packages using SSIS to extract data from staging tables to partitioned tables with incremental load
Extensively used SQL queries to check storage and accuracy of data in database tables and utilized SQL for querying the SQL database