Poojitha Sai Bobba

About Poojitha Sai Bobba

Data Engineer with 4+ years of experience building scalable data platforms and cloud-based data pipelines across healthcare, financial, and retail industries. Experienced in designing ETL/ELT pipelines, lakehouse architectures, and analytics-ready datasets using Azure and AWS technologies.I have hands-on experience with Azure Data Factory, Databricks, PySpark, SQL, Azure Synapse, and AWS services such as S3, Glue, and Redshift. My work focuses on building reliable data pipelines, optimizing distributed data processing, and enabling high-quality data for analytics, reporting, and machine learning.Currently working as a Data Engineer at Johnson & Johnson, developing scalable ingestion pipelines and lakehouse solutions to support enterprise analytics. Previously worked at Morgan Stanley building financial data pipelines and at Lowe’s developing analytics and reporting solutions.My strengths include:• Building scalable ETL pipelines using Azure Data Factory and PySpark• Designing lakehouse architectures using Azure Data Lake and Delta Lake• Data modeling and analytics-ready dataset development• Spark optimization and large-scale data processing• Cloud data platforms across Azure and AWSI am passionate about building efficient data systems that enable organizations to make better data-driven decisions.

Experience

1. Data Engineer

   Johnson & Johnson

   Sep 2024 — Present · US

   Designed and implemented scalable data ingestion pipelines using Azure Data Factory and Databricks to support enterprise healthcare analytics platforms.• Built lakehouse architecture on Azure Data Lake Storage (ADLS Gen2) using Delta Lake, enabling high-performance analytics and improving query performance for large healthcare datasets.• Developed PySpark-based transformation frameworks and automated Python validation scripts to standardize data cleansing and improve data accuracy across analytics workflows.• Implemented incremental data loading strategies using Change Data Capture (CDC) and watermarking techniques to reduce redundant processing and improve pipeline efficiency.• Built reusable and parameterized Azure Data Factory pipelines to accelerate onboarding of new enterprise data sources while maintaining governance standards.• Optimized Spark workloads through partitioning strategies and cluster configuration tuning to improve distributed processing performance.• Implemented secure data access using role-based access controls (RBAC) and Azure Key Vault to ensure regulatory compliance and protect sensitive healthcare data.• Integrated Event Hub streaming with Databricks Structured Streaming to enable near real-time data ingestion for operational dashboards and monitoring systems.• Automated CI/CD deployment workflows using Azure DevOps to improve release reliability and streamline data engineering delivery processes.• Designed dimensional models and metadata logging frameworks in Azure Synapse to support enterprise reporting, analytics, and executive decision-making

Education

• Northeastern University

  Masters, Analytics

• Gayatri Vidya Parishad College of Engineering for Women, Madhurawada

  Bachelor of Engineering - BE, Computer Science