Vaishnavi Varma Alluri

• Designed and implemented highly scalable data pipelines on Azure Data Factory (ADF) to ingest, transform, and load vehicle data from various sources (e.g., sensor data, telemetry data, sales data) into Azure Data Lake Storage (ADLS) and Azure Blob Storage
• Developed and maintained data pipelines using Spark jobs on Azure Databricks to process large volumes of real-time and historical vehicle data for analytics at scale
• Implemented data quality checks and transformations within the pipelines to ensure data accuracy, consistency, and completeness for downstream applications
• Collaborated with cross-functional teams to understand business needs and translate those needs into technical data solutions using Azure services

Keywords: Azure Data Factory, Data Pipelines, Vehicle Data, ADLS, Azure Synapse, Azure Databricks, Spark, Real-Time Data, Big Data Processing, Data Quality, Data Transformation, Data Cleaning

• Developed and maintained data pipelines in a Python/PySpark environment to transform complex healthcare data sets into business-ready formats, ensuring data accuracy and integrity
• Collaborated with product owners, analysts, and developers in an Agile Scrum framework to define requirements, refine solutions, and validate data integrations
• Analyzed and documented new and existing data interfaces within the platform, ensuring efficient data flow and adherence to data governance standards like HIPAA
• Troubleshooted complex data issues related to data quality, consistency, and completeness, implementing solutions to improve data integrity and reliability

Keywords: Python, PySpark, Data Pipelines, Healthcare Data, Agile, Scrum, Collaboration, Data Integration, Data Interfaces, Data Analysis, Data Governance, Data Troubleshooting, Data Quality, Data Integrity

• Implemented data security best practices on Azure, ensuring compliance with HIPAA regulations and protecting sensitive patient information
• Designed data models aligned with healthcare industry standards (e.g., HL7 FHIR) to ensure data consistency and facilitate data exchange between different healthcare systems within the client's environment
• Developed and implemented data pipelines using Azure Data Factory (ADF) to ingest, transform, and load healthcare data from various sources like Electronic Health Records (EHR) and claims data) into Azure Data Lake Storage (ADLS)
• Communicated complex technical concepts related to data pipelines, data architecture, and Azure services to both technical and non-technical audiences like healthcare stakeholders

Keywords: Azure Data Factory, Data Pipelines, Healthcare Data, ADLS, Azure Synapse, Azure Databricks, Spark, Big Data Analytics, Healthcare Insights, Data Modeling, HL7 FHIR, Healthcare Data Standards, Data Security, HIPAA Compliance, Healthcare Data Privacy

• Utilized Python libraries like Pandas, NumPy, and SciPy to perform advanced statistical analysis on clinical trial data (e.g., descriptive statistics, hypothesis testing, regression analysis)
• Developed data visualization dashboards using Python libraries like Matplotlib or Seaborn to effectively communicate complex clinical trial data insights to a scientific audience
• Automated repetitive data analysis tasks using Python scripts, improving efficiency and reducing the risk of human error
• Designed and implemented a series of SQL scripts to automate data cleaning tasks within the clinical trial management system (CTMS) which increased data cleaning efficiency by 9%
• Developed a data visualization dashboard using Python that reduced the time required for stakeholders to understand key clinical trial findings

Keywords: Python, Pandas, NumPy, SciPy, Statistical Analysis, Clinical Trial Data, Python, Data Visualization, Dashboards, Clinical Trial Insights, Python Scripting, Data Analysis Automation, Efficiency, Collaboration, Biostatisticians, Statistical Methods, Data Interpretation