Divya Gorantla

• Populating US healthcare data with financial resources, capital from the government, medical and academic capabilities, and characteristics of the patient population in SQL, constructing databases, and producing visuals with Python and Power BI.
• Evaluated large datasets, used data mining tools and statistical procedures.
• Collecting raw data from various sources, including databases, spreadsheets, and external APIs.
• Conducting exploratory data analysis (EDA) to identify patterns, trends, and outliers.
• Worked together with other departments to obtain the data needed, then analyzed the data to find trends, patterns, and abnormalities.
• Help with data transformation and purification to guarantee accuracy and data for reporting.
• Writing SQL queries to extract and manipulate data from relational databases.
• Working with databases like MySQL, PostgreSQL, or SQL Server.
• Coordinated a team of 30 students on a project to modify existing RDBMS, including coordinating the development of a framework to generate ad-hoc reports using enterprise data from Big Query.
• Created comprehensive reports based on MIS data to provide insights into process performance.
• Create, update, and expand standard operating procedures (SOPs) as well as BI tools.
• Preparing regular reports summarizing key performance indicators (KPIs) and data trends.
• Took part in conversations to get input and improve your data analysis.

Populating US healthcare data with financial resources, capital from the government, medical and academic capabilities, and characteristics of the patient population in SQL
Constructing databases
Producing visuals with Python and Power BI
Evaluated large datasets
Used data mining tools and statistical procedures
Collecting raw data from various sources, including databases, spreadsheets, and external APIs
Conducting exploratory data analysis (EDA) to identify patterns, trends, and outliers
Worked together with other departments to obtain the data needed, then analyzed the data to find trends, patterns, and abnormalities
Help with data transformation and purification to guarantee accuracy and data for reporting
Writing SQL queries to extract and manipulate data from relational databases
Working with databases like MySQL, PostgreSQL, or SQL Server
Coordinated a team of 30 students on a project to modify existing RDBMS, including coordinating the development of a framework to generate ad-hoc reports using enterprise data from Big Query
Created comprehensive reports based on MIS data to provide insights into process performance
Create, update, and expand standard operating procedures (SOPs) as well as BI tools
Preparing regular reports summarizing key performance indicators (KPIs) and data trends
Took part in conversations to get input and improve your data analysis

Designed ETL process using Pentaho Tool to load from Sources to Targets with Transformations
Worked on Snowflake Schemas and Data Warehousing
Worked within Agile methodologies using Jira Agile boards, managing sprints, and participating in ceremonies
Developed Pentaho Big data jobs to load heavy volume of data into Amazon S3 data lake and then into Amazon Redshift data warehouse
Actively participated in continuous improvement initiatives for data quality enhancement, providing valuable insights for process optimization
Developed Ad-hoc reports and designed dashboard using splunk
Managed and prioritized product backlogs, translating requirements into detailed user stories with acceptance criteria for development teams
Migrated the data from Redshift data warehouse to Snowflake database
Interpreting data and providing insights to support decision-making processes
Collaborating with team members to understand business requirements
Participating in team meetings and contributing to discussions
Conducting ad hoc analysis as requested by team members or management
Responding to specific data-related queries from stakeholders
Created custom reports and dashboards within CRM platforms to track and visualize key metrics
Used tools like Power BI or Tableau to enhance data visualization for stakeholders
Build dimensional modelling, data vault architecture on Snowflake
Loading the data from the different Data sources like (Teradata, DB2, Oracle and flat files) into HDFS using Sqoop and loading into Hive tables, which are partitioned
Implemented data warehousing solutions using Snowflake, taking advantage of its cloud-based architecture
Integrated Lambda functions with other AWS services, such as API Gateway and S3

Collecting and integrating data from various sources within the supply chain, including inventory systems, logistics databases, and procurement platforms
Applying statistical models and machine learning algorithms for demand forecasting to improve inventory management and reduce stockouts
Implementing algorithms and models to optimize transportation routes, reduce delivery times, and minimize transportation costs
deploying code to leading cloud platforms, including Azure, GCP, and AWS, leveraging Docker/Kubernetes containers for efficient and scalable deployments
Assessing the performance of machine learning models using appropriate metrics
Validating models and adjusting parameters for optimal results
Developing risk models to identify and mitigate potential risks in the supply chain, such as geopolitical events, natural disasters, or supplier financial instability
Assessing the performance of machine learning models using appropriate metrics
Validating models and adjusting parameters for optimal results
Implemented Agile practices that are scalable to accommodate larger project teams and complex project landscapes
deploying code to leading cloud platforms, including Azure, GCP, and AWS, leveraging Docker/Kubernetes containers for efficient and scalable deployments
Applied sophisticated modeling methodologies such as VAR, Dynamic VAR, and Fourier Transformation for comprehensive data analysis
Demonstrated expertise in containerization technologies, utilizing platforms like Docker to deploy and manage cloud services
Successfully deployed code that scales to meet the demands of various cloud environments, accommodating dynamic workloads and user interactions

Developed Ad-hoc reports and designed dashboard using Splunk
Worked with the management team and gathered the data from multiple data resources and then developed and designed ETL solutions as required
Proficient in working with Customer Relationship Management (CRM) systems such as Salesforce, HubSpot, or other relevant platforms
Conducted comprehensive data quality assessments, identifying discrepancies and implementing corrective measures to enhance the accuracy and reliability of datasets
Developed reports and dashboards to visualize key performance indicators (KPIs) related to customer interactions and sales activities
Writing ETL codes and SQL queries to check the desired results in the data-driven workflows and processing the transformation and loading
Followed Waterfall methodology in the development process and participated in the daily Scrum meetings and carried sprints biweekly
Developed automated test scripts using Selenium WebDriver with programming languages such as Python
Developed reports and visualizations using BI reporting tools like Tableau and produced worksheets and dashboards
implementation of data governance policies and frameworks to ensure standardized data management practices across the organization
Performed the Data modeling to develop the mappings accordingly and involved with the Data Modelling team and provided suggestions in creating the data model
Experience working with cloud-based data storage solutions, such as AWS S3, Azure Blob Storage, or Google Cloud Storage
Utilized cloud services for data processing, transformation, and analysis
Executed test cases directly within the qTest platform, recording test results and tracking test progress
Designed and Developed Informatica Mappings from Scratch to Load the Data from Source System to Staging system and Warehouse System
Writing ETL codes and SQL queries to check the desired results in the data-driven workflows and processing the transformation and loading
Utilized Agile metrics (e.g., velocity, burn-down charts) to track team progress and provide insightful reporting to stakeholders
Performed validation, quality checks, and audit count at various stages of the ETL process using data threshold validation check