Praneeth Sai Chippa

AWS Data Engineer at Saibersys, Dallas, Texas, USA

Implemented GitOps workflows using Azure Repos and GitHub for automated code reviews and deployment safety. Developed and optimized ETL pipelines with AWS Glue, PySpark, and Redshift, automating schema evolution and metadata cataloging. Engineered real-time and batch data processing workflows using AWS Kinesis, Lambda, and EMR to support high-volume streaming data ingestion for operational dashboards and analytics. Enhanced data quality with Python-based testing frameworks and automated error handling, reducing inconsistencies in migrated systems. Managed containerized deployments using Docker, Kubernetes, Rancher, and AWS Fargate for improved scalability and microservices deployment. Collaborated closely in Agile teams contributing reusable code templates and migration playbooks.

Data Engineering & Analytics Associate at Midwestern State University, Texas, USA

Built and optimized end-to-end data pipelines with Python, R, and AWS services to support machine learning models, predictive analytics, and research initiatives. Applied scikit-learn and statistical techniques for predictive modeling and insights generation. Designed dashboards using Power BI, Tableau, and Excel integrating cloud and on-premises data. Supported automation using cloud-based workflows and infrastructure as code.

Data Engineer Associate at Accenture Solutions Pvt Ltd, Hyderabad, India

Architected and maintained scalable data pipelines using Python, SQL Server, Apache Spark, AWS Glue, and EMR for enterprise datasets. Led real-time streaming solutions with AWS Kinesis for timely analytics. Designed ETL workflows and data models in AWS Glue and Redshift improving data accuracy and business intelligence readiness. Delivered dashboards in Power BI and Tableau enhancing leadership insights and automating data refresh schedules for real-time metrics.

Data Analytics Engineer at Accenture Solutions Pvt Ltd, Hyderabad, India

Developed and maintained ETL pipelines using SQL, Python, and AWS Glue, consolidating customer data into centralized warehouses. Delivered real-time dashboards in Power BI and Tableau for sales and customer engagement KPIs. Implemented AWS Kinesis streaming workflows reducing reporting delays. Optimized queries and data quality checks to improve insight accuracy across business operations.

Full Stack Engineering Analyst at Midwestern State University

Built end-to-end AI search interfaces integrating back-end APIs for document retrieval and result visualization. Deployed Flask-based microservices connecting Elasticsearch with machine learning inference layers for real-time hybrid search. Used OCR and NLP to extract structured insights from PDFs and Excel documents. Optimized search latency with caching and parallel processing.

Data Analytics Associate at Accenture Solutions

Implemented machine learning models using TensorFlow and scikit-learn to classify, extract, and retrieve document-level data. Designed indexing workflows combining text embedding and keyword-based search for improved information retrieval accuracy. Automated data cleaning and feature extraction for hybrid documents using NLP preprocessing. Supported AI teams in testing and refining search features, contributing to faster query resolution and optimized model performance.

at Cognizant Solutions

Hobbies: Cycling, Chess, Travel.

Data Analytics Associate at Midwestern State University

Built end-to-end AI search interfaces; deployed Flask-based microservices connecting Elasticsearch with machine learning inference layers for real-time hybrid search. Worked with PyTorch models for text embeddings and document similarity ranking across large datasets. Optimized search latency and throughput through caching, efficient indexing, and parallelized query handling.

Machine Learning Engineer at Accenture Solutions

Implemented machine learning models using TensorFlow and scikit-learn to classify, extract, and retrieve document-level data. Deployed Flask-based microservices connecting Elasticsearch with machine learning inference layers for real-time hybrid search. Worked with PyTorch models for text embeddings and document similarity ranking across large datasets. Optimized search latency and throughput through caching, efficient indexing, and parallelized query handling.