Hello World, I'm an aspiring Software Engineer located in Portland!
Welcome to my portfolio site, feel free to look around :)
Resume Get in touch
Hi, my name is Sandeep
(he/him/his)
I am a highly motivated and dedicated Computer Science professional with a Master's degree from Portland State University. Skilled in Python, JavaScript, databases (PostgreSQL), and platforms (Google Cloud, AWS). Experienced in IT support, Proficient in application support, continuous improvement, and quality assurance. Certified AWS Cloud Practitioner with a passion for Cybersecurity. Accomplished various projects in movie recommendation, database management, machine learning, and web design. Seeking opportunities to leverage skills, collaborate, and contribute to the field of Computer Science.. If you think that my skills can add value to your company please feel free to reach out!
Education
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Portland State University
Master of Science, Computer Science 3.91 GPA, Graduated August,2023
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Sree Nidhi Institute Of Science and Technology, Hyderabad, India
Bachelor of Technology, Computer Science 87% Aggregate
Work
- Provided technical support for PSU faculty, staff, and students on Windows and MacOS. Supported multiple departments and various offices in PSU through phones, chat, online ticketing system, and in-person
- Troubleshooted technology issues and documented customer requests using JIRA, Confluence, banner, and Data Master thereby reducing the incidents by 25%
- Obtained knowledge of SailPoint IIQ to support role-based access control and used OAM (Oracle Access Manager) to reset passwords, add MFA devices, and managed service accounts for the organization
- Worked collaboratively in a team-oriented environment and provided timely, thorough, and professional customer service
- Maintained composure and patience in facing difficult customer situations, applying de-escalation techniques and providing positive customer support
- Built and maintained a highly scalable data pipeline that processed over 10TB of data per day with near-realtime latency, resulting in a 20% increase in processing efficiency.
- Spearheaded a Big Query-based analytics platform that allowed the data science team to generate complex reports on customer behavior, resulting in a 15% reduction in customer churn rate..
- Optimized Dataflow jobs to reduce average processing time by 30%, resulting in significant cost savings for the company.
- Revamped existing GCP Data Management systems for multiple clients, leveraging automation tools to improve data accuracy by 25% and reduce manual errors by 70%.
- Led the design of 20 IaaS-based solutions in Google Cloud, saving $30K in costs.
- Orchestrated Docker and Kubernetes, optimizing deployment processes with a 20% error reduction.
- Boosted database performance from 10% to over 30% with dynamic auto-scaling solutions.
- Architected and managed GCP infrastructure, realizing a 30% system performance boost.
- Enhanced response time to critical errors by 91%, reducing server downtime by 30% through AWS Cloud Infrastructure.
- Operationalized AWS big data stack, boosting data processing efficiency by a staggering 90%.
- Automated AWS stack creation with CloudFormation templates, elevating team efficiency by 30%.
- Achieved a 70% system performance improvement with Elastic Load Balancing and Auto Scaling.
- Implemented containerization strategies via AWS ECS, driving efficiencies up by 50%.
- Deployed Terraform for Infrastructure as Code, slashing resource provisioning time by 25%.
- Designed and streamlined GCP data solutions resulting in a 30% growth in customer satisfaction.
- Built ETL /ELT data pipelines resulting in 25% faster data processing times.
- Orchestrated the successful deployment of a cutting-edge data warehousing and analytics platform; enabled clients to uncover actionable insights, resulting in a 40% improvement in operational efficiency and a 10% increase in customer satisfaction ratings
- Orchestrated collaborative efforts with cross-functional teams of architects and technical consultants, resulting in successful implementation of innovative solutions, driving a 20% surge in customer satisfaction scores
- Maintained 99.9% availability while administering and monitoring GCP resources.
- Implemented robust backup strategies for Cloud SQL databases, ensuring data integrity.
- Configured network settings, VPCs, and Cloud Load Balancing, optimizing network performance by 15%.
- Offered valuable guidance to customers on GCP cloud, Linux, and configuration, further enhancing integration smoothness.
- Proactively troubleshooted and resolved cloud-related incidents, minimizing downtime and ensuring business continuity.
- Enhanced team automation and efficiency by 65% through AWS IAM, managing user accounts and access control.
- Achieved 100% customer satisfaction by implementing advanced troubleshooting techniques tailored to unique AWS customer needs.
- Improved system uptime by 25% with sophisticated AWS CloudWatch and CloudTrail-based monitoring.
- Spearheaded the utilization of AWS RDS for database management, realizing a remarkable 60% efficiency boost.
- Provided expert advice on cloud, Linux, and configuration, contributing to a 33% increase in integration smoothness.
Projects
Movie Rating and Recommendation
This is a Movie recommendation and reviews application which takes the movie name as input and presents with the ratings. Also we get movie recommendations which are refreshed every day. Built in MVP architecture by integrating REST API from IMDb, TMDB, and Giphy Python packages. Built a docker container using Google Cloud and pushed the docker image to the container registry and hosted the application on cloud run using datastore as the backend.
Transit Systems Of World Database
Transit Systems of world database consists of different datasets (csv format). The source of the data is from Kaggle. The database contains information about transportation line data from a number of cities around the world including London, Berlin, Mexico City, Barcelona, Washington D.C., and others covering many thousands of kilometers of lines including corresponding cities and systems. It also includes station names and tracks for those cities.
TB Detection using Chest X-rays
In this project, On Tuberculosis data, we offer a machine learning technique to compare the performance of K Nearest Neighbors and an ensemble of classifiers. Real data was used to train the categorization models. We have used Local KNN, Weighted KNN and Gradient Boosting to predict the disease in patients and we have compared their accuracies and found the best for our problem. The classifiers' prediction accuracy was assessed using k-fold Cross Validation, and the results were compared to determine the best prediction accuracy.
Social Media Aggregator Design
This project presents the final high-fidelity interface prototype for our mobile social media aggregator app and discusses the justification of design decisions based on our initial research, design principles, analytical and empirical evaluations for the prototype. This was done in order to address usability problems with modern social media platforms and improve the experience for users of multiple social media platforms.