Ravindra Daggupati
Data Engineer @Tenet Healthcare
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WORK HISTORY
Data Engineer @Tenet Healthcare
Dallas, TX, US
As an AWS Data Engineer at Tenet Healthcare (Prime Solutions), I design and build scalable, cloud-native data solutions to support enterprise healthcare analytics. I specialize in developing robust ETL/ELT pipelines using AWS Glue, Python, and SQL to process large-scale datasets including claims, provider, and member data.I have architected centralized data lake solutions on Amazon S3 integrated with AWS Lake Formation and Glue Data Catalog, enabling secure, governed, and high-performance data access. I also developed optimized data warehouse solutions on Amazon Redshift, improving query performance and supporting critical business reporting.In addition, I built real-time streaming pipelines using Apache Kafka, AWS Kinesis, and Spark Streaming, reducing data latency and enabling near real-time insights. My work includes leveraging AWS EMR, Spark, and PySpark for distributed data processing and implementing monitoring solutions for pipeline reliability.I have also contributed to predictive analytics initiatives by integrating machine learning models for cost prediction, anomaly detection, and risk scoring. Through interactive dashboards and reporting, I help drive data-driven decision-making and operational efficiency across healthcare systems.
EDUCATION
Qis Institute of technology
Bachelor of Technology - BTech, Electrical, Electronic and Communications Engineering Technology/Technician
Saint Peter's University
Master's degree, Data Science
ABOUT RAVINDRA DAGGUPATI
Data Engineer with 5+ years of experience building scalable data platforms, high-performance ETL pipelines, and real-time analytics systems across AWS, GCP, and distributed environments.I specialize in designing data architectures that handle large-scale structured and unstructured data using technologies like Apache Spark, Kafka, and cloud-native services (S3, Glue, Redshift, BigQuery). My work focuses on improving performance, scalability, and reliability of data systems that drive business decisions.I also have hands-on experience integrating AI/ML models into data pipelines using TensorFlow, PyTorch, and MLflow to enable predictive analytics and intelligent data workflows.Recently, I’ve been aligning my experience with modern platforms like Palantir Foundry, focusing on data lineage, pipeline orchestration, and governed data ecosystems. Core Strengths:• ETL/ELT Pipelines & Data Engineering• Real-time Streaming (Kafka, Spark Streaming)• AWS Data Stack (S3, Glue, Redshift, Lambda)• Distributed Systems & Microservices• AI/ML Pipeline Integration• Data Warehousing & Data LakesI’m actively exploring opportunities where I can contribute to building scalable, data-driven platforms and solving complex engineering challenges.
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