Python, AWS,Django,Spark,SQl

Name:Uma Maheshwar Gupta Gunda

Email Id: **********@*****.***

Phone No: +1-747-***-****

Professional Summary:

A Python Full-Stack AWS Developer with over 7 years of experience in designing and implementing scalable, secure, and high-performing cloud-native solutions. Proficient in developing full-stack web applications using Python frameworks (Django, Flask), deploying containerized microservices, and building serverless architectures with AWS. Skilled in creating robust CI/CD pipelines using AWS Code Pipeline and Terraform, optimizing cloud infrastructure for cost and performance. Demonstrated expertise in database management (PostgreSQL, MongoDB), API development, and building data pipelines for ETL workflows. Proven ability to align technical solutions with business objectives, delivering impactful and quantifiable results.

Technical Skills:

Cloud Technologies

AWS (EC2, Lambda, S3, RDS, Beanstalk, CloudFormation, SageMaker, Glue, Redshift), Azure, Docker, Kubernetes

Frameworks

Django, Flask, PySpark, TensorFlow, Keras, PyTorch

Web Technologies

HTML, CSS, JavaScript, jQuery, AJAX, XML, Bootstrap

Programming Languages

Python, SQL and PL/SQL.

Data Science/Analysis Tools

Tableau, Power BI, Pandas, NumPy, Matplotlib, SciPy, scikit-learn

Scripting languages

CSS, AJAX, Java Script, JQuery, PowerShell Scripting.

Databases

Oracle, My SQL, Apache Cassandra, MongoDB, IBM

IDE’s/ Development Tools

PyCharm, Sublime Text, Eclipse, NetBeans, PyScripter, PyStudio, Vscode

CI/CD Tools/ Version Control

Jenkins, Git, GitLab CI/CD, GitHub Actions

Protocols

HTTP, HTTPS, TCP/IP

Operating Systems

Linux, Unix, Windows

Testing Tools

Selenium, Bugzilla, Crucible and JIRA.

Methodologies

Agile, Scrum and Waterfall.

Professional Experience:

Charles Schwab, San Francisco, CA

Duration: Feb 2023 - Present

Role: Senior Python Full-Stack AWS Developer

Project: Financial Data Analysis Platform – Developed a secure, scalable, and robust financial data platform to enable real-time data analysis and visualization for enterprise decision-making.

Created and maintained CI/CD pipelines using AWS CodePipeline and Terraform, incorporating automated tests and deployment workflows, reducing deployment time by 30% and minimizing human errors.

Deployed backend services in a serverless architecture using AWS Lambda and ECS with auto-scaling configurations, improving response times during peak traffic by 50% and maintaining 99.9% uptime.

Integrated AWS S3 for secure data storage, implementing lifecycle policies for archival and retention, achieving 25% cost savings and ensuring data durability and accessibility.

Configured and implemented secure AWS VPC architectures, IAM roles, and security groups, ensuring 100% compliance with financial data security regulations like SOX and GDPR.

Automated end-to-end testing using Selenium, PyTest, and Behave, achieving a defect rate below 0.5% across production releases and increasing overall application reliability.

Migrated legacy applications to serverless architecture, leveraging AWS Fargate and CloudFormation templates, reducing infrastructure costs by 20% and enhancing scalability.

Improved API latency by 30% through advanced caching strategies using Redis and Elasticache, and asynchronous data processing pipelines.

Developed robust monitoring solutions with AWS CloudWatch, creating custom alarms and metrics to track performance and reduce incident resolution time by 40%.

Optimized database schemas and queries in PostgreSQL, implementing indexing and partitioning strategies to handle high-volume transactions, reducing query execution times by 35%.

Built containerized applications using Docker and Kubernetes, enabling consistent deployments across development, staging, and production environments while improving deployment efficiency.

Designed and implemented asynchronous task queues with Celery and RabbitMQ for long-running processes, ensuring seamless user experience without delays in UI response.

Worked collaboratively in an Agile environment, using JIRA and Confluence for task tracking and documentation, while conducting code reviews to maintain high-quality standards.

Integrated OAuth2 and JWT-based authentication for secure API access, alongside implementing input validation and request throttling to prevent malicious activity and ensure system integrity.

Environment: Python, Django, Flask, React.js, PostgreSQL, AWS (Lambda, EC2, ECS, S3, RDS, CloudWatch, IAM, SNS, SQS), Terraform, Docker, Kubernetes, CircleCI, AWS CodePipeline, Redis, RabbitMQ, Selenium, PyTest, Behave, JIRA, Confluence.

Warner Bros. Discovery (Loginsoft Pvt Ltd-Contract), Hyderabad, India

Duration: Aug2021 – Jul 2022

Role: Senior Python Full-Stack AWS Developer

Project: Cloud-Based Media Workflow Orchestration Platform – Built and optimized serverless media workflow solutions to streamline OTT content processing and improve partner onboarding, enabling scalable, automated media management.

Designed and developed a serverless cloud-based media workflow orchestration solution using AWS Lambda, Step Functions, and DynamoDB integrated with SDVI Rally, reducing manual media transcoding time by 60%.

Engineered advanced NLP algorithms in Python for video metadata processing, deployed in a serverless architecture, improving content tagging accuracy by 40% and enhancing media searchability.

Developed custom Python scripts for automated video metadata extraction and processing, streamlining workflows and reducing manual data entry by 70%.

Created scalable OTT Python Presets deployed on AWS Lambda and SDVI Rally, increasing media workflow efficiency by 35% and enhancing partner integration by 70%.

Built high-performance ETL pipelines using AWS Glue and Step Functions, improving data transformation speed by 55% for large-scale media datasets.

Leveraged NumPy and Pandas for statistical analysis of media consumption patterns, generating insights that led to a 25% increase in content engagement.

Automated CI/CD pipelines using Jenkins and AWS CodePipeline, reducing release cycles by 60% and improving application stability by 40%.

Optimized database queries and structures in PostgreSQL and Amazon RDS, enhancing query performance by 45% for large-scale media asset management.

Developed serverless media processing solutions using AWS Lambda, automating transcoding tasks and reducing manual workflows by 50%, improving OTT partner efficiency.

Integrated machine learning models for content recommendation using TensorFlow and collaborative filtering techniques, achieving 30% higher accuracy.

Contributed to the design of a scalable microservices architecture with Python orchestration and Java components within AWS ECS, reducing infrastructure provisioning time by 50% using Terraform.

Provided L3 production support for critical media systems, creating automated troubleshooting scripts and reducing mean time to resolution (MTTR) by 30%.

Environment: Python, SQLAlchemy, SDVI Rally Workflow Orchestration, NLP, Pandas, NumPy, Pytest, Terraform, CI/CD (Jenkins, AWS CodePipeline), AWS (Lambda, Step Functions, ECS, DynamoDB, S3, Glue), Docker, Kubernetes, PostgreSQL, TensorFlow, ETL, RESTful APIs, Machine Learning, Automation Scripting.

Sonata Software, Hyderabad, India

Duration: May2017 – August 2021

Role: Python Full-Stack AWS Developer

Project 1: E-Commerce Personalization Platform – Designed a scalable platform to personalize shopping experiences, increasing user engagement and driving sales conversions for a global e-commerce client.

Developed scalable RESTful APIs using Django, enabling personalized product recommendations for over 500K daily active users.

Integrated real-time ML models, improving recommendation accuracy by 45% and boosting conversion rates by 30%.

Built a user-friendly front end with React.js and Bootstrap, enhancing user interaction with a 30% increase in engagement.

Automated ETL pipelines with Apache Airflow and AWS Glue, processing terabytes of user behavior data daily for actionable insights.

Deployed serverless inference endpoints with AWS Lambda, achieving sub-second response times for recommendation requests.

Implemented caching strategies using AWS Elasticache, reducing API latency by 40%.

Automated CI/CD pipelines using Terraform and Jenkins, decreasing deployment time by 50%.

Integrated APIs with PostgreSQL and DynamoDB, optimizing query performance and achieving 25% faster database response times.

Monitored application health using AWS CloudWatch, reducing downtime by 35% through proactive issue detection.

Designed and deployed a fault-tolerant system with AWS Auto Scaling and Load Balancing, ensuring 99.9% uptime.

Led A/B testing for algorithm optimization, achieving a 20% improvement in user satisfaction.

Optimized AWS infrastructure, reducing overall costs by 40% through intelligent resource allocation.

Project 2: Options Trading Platform – Designed and developed a real-time options trading platform to process market data, provide actionable insights, and execute trades seamlessly, catering to the needs of high-frequency traders.

Designed and deployed Mathematical models for option prices calculation using NumPy, SciPy and Pandas, achieving 85% accuracy for short-term market movements.

Built real-time data ingestion pipelines using AWS Kinesis and Lambda, handling millions of data points daily with near-zero latency.

Developed a robust microservices architecture on AWS ECS, ensuring 99.99% uptime during high-traffic trading periods.

Integrated sentiment analysis using AWS Comprehend, improving trading decision accuracy by 20%.

Created a secure API gateway for managing sensitive financial data, implementing OAuth2 for enhanced security.

Automated testing and deployments using PyTest and AWS CodeBuild, ensuring 99.5% reliability in production.

Migrated legacy trading systems to AWS infrastructure, reducing latency by 60% and achieving 40% cost savings.

Optimized real-time query performance with PostgreSQL and DynamoDB, reducing response times by 30%.

Designed scalable dashboards with Tableau and React.js for real-time market insights, improving decision-making speed by 25%.

Built fault-tolerant systems with AWS Auto Scaling, ensuring seamless performance during peak trading hours.

Implemented data encryption with AWS KMS, ensuring compliance with financial regulations.

Collaborated with data scientists to deploy trading algorithms, enhancing platform efficiency by 50%.

Project 3: Enterprise Content Management System (CMS) – Developed a CMS to streamline document management and approval workflows for a large enterprise, improving operational efficiency.

Designed and implemented a modular CMS using Django and PostgreSQL, enabling seamless document storage and retrieval for over 10K users.

Developed intuitive UI components with React.js and Bootstrap, improving user experience by 40%.

Automated document indexing and metadata extraction using Python, reducing manual processing time by 70%.

Built secure API integrations with third-party services, enabling real-time data exchange and reducing workflow delays by 50%.

Deployed containerized services with Docker and Kubernetes on AWS ECS, ensuring 100% environment consistency.

Streamlined CI/CD pipelines with GitLab and Terraform, reducing deployment cycles by 60%.

Implemented database optimization techniques in PostgreSQL, improving query execution speed by 30%.

Enhanced security with AWS IAM, encrypted sensitive data with KMS, and implemented S3 lifecycle policies for cost-effective storage.

Created ETL workflows with Apache Airflow, automating data updates and ensuring timely reporting.

Designed dynamic dashboards with Power BI, providing real-time insights into document approval metrics.

Reduced infrastructure costs by 25% with efficient resource utilization and scaling policies.

Conducted training sessions for end-users and documented best practices, ensuring smooth adoption of the CMS across teams.

Environment: Python, Django, PostgreSQL, MongoDB, MS SQL Server, Apache Airflow, RabbitMQ, Tableau, Power BI, Alpha Vantage API, AWS (Glue, Lambda, S3, RDS, DynamoDB, Kinesis, API Gateway, CloudFormation, ECS, CodePipeline, CloudWatch, IAM, VPC, EC2), Docker, Kubernetes, Nginx, Gunicorn, Ansible, Jupyter Notebooks, Git, GitLab CI/CD, Jenkins, Selenium, PyTest, NumPy, SciPy, Pandas, JIRA, HTML5, CSS, JavaScript, Bootstrap, Linux, Shell Scripting, RESTful APIs, Swagger, Agile, Scrum.

Education:

University of Maryland– College Park, MD, USA

Duration: Aug2022 – December 2023

Degree: Master’s in Information systems(GPA: 3.78/4)

Coursework and Achievements:

Terrapin Scholar

Coursework: Big data & AI, Data Processing and Analysis using Python, Data Mining and Predictive Analytics, Data Models and decisions, Database Management Systems, Managing Digital Businesses, Business Process Analysis, Project Management and, Digital Transformation in Business

Shiv Nadar University, Noida, India

Duration: Aug2013 – May 2017

Degree: Bachelor’s in Mechanical engineering (GPA: 6.99/10)

Coursework and Achievements:

Specialization in Computational Mechanics

Coursework: Computational Fluid Dynamics, Problem solving though programming using C language

Contact this candidate