Hemanth Challagundla

About Hemanth Challagundla

Architected and Deployed GCP-based Data Solutions: Led the design and implementation of end- to-end data solutions on Google Cloud Platform (GCP), utilizing key services such as BigQuery, AlloyDB, Dataproc, Cloud Dataflow, and Cloud Storage (GCS). These solutions enabled scalable and efficient data processing across various business functions, reducing data latency by 30%. • Developed Robust ETL Pipelines: Engineered and optimized ETL workflows using Cloud Dataflow and Dataproc, handling complex data transformations for structured and unstructured data sources. Leveraged Apache Spark (Scala) for large-scale batch processing, achieving a 40% reduction in processing time. • Designed and implemented Azure Synapse Analytics solutions to integrate data from multiple sources, enabling seamless data processing and analytics. • Designed and implemented end-to-end data pipelines within Synapse for data ingestion, transformation, and loading (ETL) from various sources including Azure Data Lake, SQL Data Warehouse, and on-premises databases. • Enhanced Data Governance and Security Established a comprehensive data governance framework using Cloud IAM, VPC Service Controls, and encryption mechanisms. • Implemented role-based access controls (RBAC) and data masking techniques, ensuring compliance with regulatory standards like GDPR and CCPA. • Leveraged Cloud Composer for Workflow Orchestration: Automated the scheduling and monitoring of data pipelines using Google Cloud Composer (Airflow). Built DAGs for orchestrating multi-step ETL jobs, reducing manual intervention and increasing pipeline reliability. • Integrated Machine Learning Models: Collaborated with data scientists to incorporate BigQuery ML models into data workflows, enabling predictive analytics for customer behavior analysis. Deployed machine learning models for customer segmentation, resulting in targeted marketing strategies and a 25% increase in engagement. • Developed and deployed machine learning models on GCP Vertex AI for real-time prediction workflows, enabling scalable and efficient ML Ops practices. Collaborated on building Large Language Model (LLM) pipelines for text analysis and summarization tasks, enhancing customer insights with advanced AI capabilities. • Utilized Azure Synapse Studio to build and orchestrate data workflows and pipelines, integrating data from diverse sources (cloud and on-prem). • Developed and managed data pipelines with Azure Data Factory within Synapse to automate data movement and transformation, reducing manual intervention and errors.

Experience

1. Senior Data Engineer

   Cvs Pharmacy

   Sep 2024 — Present

Education

• University of Central Missouri

  Master's degree

  2021 — 2022