Someshkumar Srihari Hemanthkumar
Data Analyst, Data Engineer, Cloud Engineer
About Me
- Full Name:Someshkumar Srihari Hemanthkumar
- Phone:+1 361 455 5073
- Email:somesh1st@gmail.com
- Address:Texas, USA
Hello There!
I’m a Master’s student in Computer Science at Texas A&M University–Kingsville, specializing in Cloud Engineering, DevOps, and Data Engineering, with a strong foundation in analytics-driven problem solving.
My work sits at the intersection of cloud infrastructure, automation, and data systems. I design and build scalable, reliable platforms that turn complex workloads into efficient, production-ready solutions.
I recently attended AWS re:Invent as a Grant Participant, where I deepened my exposure to real-world cloud architectures, serverless systems, and reliability practices used at scale. This experience reinforced my interest in Cloud, DevOps, and SRE-style roles focused on automation, observability, and performance.
What I’ve Built & Researched
- Master’s Thesis: Reducing Cold Start Latency in Serverless Architectures
Designed and evaluated AWS Lambda architectures using Redis-based external caching, analyzing latency, cost, and scalability trade-offs. - Cloud & DevOps Projects:
Built cloud data pipelines, CI/CD workflows, and infrastructure automation using AWS, Terraform, containers, and monitoring tools. - Data Engineering & Analytics Experience:
Previously worked as a Data Analyst on financial fraud detection, credit risk modeling, and BI reporting, which sharpened my ability to design data systems that are both technically sound and business-aware. -
Research & Applied Analytics:
- Banking deserts analysis using QGIS and U.S. Census data
- Economic and CPA exam pass-rate trend analysis across regions
What I'm Looking For
I’m actively seeking full-time opportunities in:
- Cloud Engineering / DevOps / SRE
- Software Engineering (infrastructure & Platform focused)
- Data Engineering
- Cloud-native Analytics & Systems
I thrive in environments where I can build systems end-to-end, automate relentlessly, and collaborate across teams to solve real production problems. I’m especially excited by teams that value ownership, reliability, and continuous learning.
Certifications
Recommendations
What others say about my work
Dr. Thomas M Krueger
Chair, Accounting and Finance Department at Texas A & M University
“I had the pleasure of working with Somesh in his role as a Graduate Research Assistant, where he contributed significantly to multiple data-driven projects under my supervision. The accuracy, quality, and quantity of Somesh's work were superb. His work on banking deserts using QGIS, economic forum survey analysis, and the CPA exam performance study demonstrated both technical ability and strong analytical thinking. The Economic Forum survey analysis relied upon Somesh's collection of survey responses at Harrel's, a popular restaurant in Kingsville. This location is three miles from my office, requiring a special person for this task. I found Somesh to be very dependable and hardworking. In fact, Somesh is the best intern I have had across 14 years of Economic Forums. Somesh also played a vital role in organizing the Banking and Business Career Expo. He served as the student contact, helping them with registration and mock interview scheduling, and well as maintained the attending recruiter register. He is dependable, detail-oriented, and an excellent collaborator, and I highly recommend him for roles in data and research. Recognizing his talents, it is not surprising that Bay Ltd, one of our top recruiters, tapped him as one of their Summer 2025 interns. The number of in-office administrative tasks should also be mentioned. I could rely upon him for solutions to technology issues. This included helping me set up my courses in a new management learning system. Any information he saw from faculty recruiting documents to students' grades was always kept confidential. Somesh's graduation will be a bitter (impossible to replace), sweet (expecting him to have a great future) day!”
View on LinkedInDr. David Hicks
Associate Professor of Computer Science and Associate Chair
I have had the pleasure of working with Somesh on an NSF sponsored project (NRT-TREAWS). He showed strong initiative, technical skills, and professionalism while contributing to the early-stage development of a comprehensive web site for the project. As a thesis student, he is tackling a relevant and technically complex research area focused on reducing cold start latency in serverless cloud computing. I’m confident in Somesh’s ability to contribute meaningfully to a variety of data-driven and cloud-focused roles in industry or academia.
Highlights:
• Contributed to the initial development of the NRT-TREAWS project website.
• Demonstrated strong ownership, clear communication, and initiative during project tasks.
• Currently working under my guidance on a thesis focused on reducing cold start latency in serverless architectures using Redis and AWS Lambda.
• Highly motivated, reliable, and eager to apply academic learning to real-world problems in data and cloud domains.
Orlando Torres
Director of Business Analytics at Bay Ltd.
To Whom It May Concern, I am pleased to recommend Somesh. During their time at Bay Ltd., Somesh consistently demonstrated exceptional professionalism, dedication, and a strong work ethic. In their role as Business Analytics and Corporate Systems Intern, they were responsible for creating new business reports with Power BI and Cognos. He also helped in developing new automation between systems. Beyond their technical skills, Somesh is a natural collaborator and a positive presence in the workplace. They communicate clearly, adapt quickly, and consistently go above and beyond expectations. I do not doubt that Somesh will bring the same level of excellence to any future endeavor.
View on LinkedIn-
Work Experience
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Graduate Research Engineer
Texas A & M University — June 2024 - December 2025- Led multiple data-intensive research projects including a Banking Deserts geospatial study using QGIS and U.S. Census data, economic forum survey analysis, and CPA exam performance trend analysis—delivering high-accuracy insights from real-world datasets while maintaining strict data confidentiality.
- Completed a Master’s thesis on reducing cold start latency in serverless architectures, designing and evaluating AWS Lambda + Redis external caching solutions with a focus on performance, scalability, and cost optimization, strengthening my foundation in cloud-native systems and reliability engineering.
- Supported system operations and process reliability by assisting with learning management system setup, resolving technology issues, and coordinating the Banking & Business Career Expo (recruiter registration, scheduling, and tracking), demonstrating strong ownership, dependability, and cross-functional collaboration.
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Business Analyst & Corporate Systems Intern
Bay Ltd. - June 2025 - August 2025- Supported enterprise analytics and corporate systems by developing production Power BI and IBM Cognos reports while assisting with system-level automation and data flow reliability across platforms.
- Contributed to automation initiatives that reduced manual reporting overhead and improved consistency between financial and operational systems. Worked closely with stakeholders to validate outputs, troubleshoot issues, and support ongoing system enhancements.
- This role strengthened my foundation in enterprise systems, automation, and platform-oriented thinking, reinforcing my focus on Cloud, DevOps, and infrastructure-driven analytics.
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Cloud Data Engineer
Bootstrap Sports Retail India Pvt. Ltd. - June 2023 - May 2024- Engineered automated, cloud-based ETL pipelines using Python, PySpark, SQL, and Airflow to ingest and process sports performance data from distributed APIs, emphasizing pipeline reliability, scalability, and fault tolerance.
- Designed analytical schemas in Snowflake and MySQL to support reporting and ML workloads, while building Power BI dashboards for real-time athlete performance monitoring.
- Applied machine learning models to forecasting use cases and partnered cross-functionally to enforce data quality, governance, and operational consistency across analytics systems.
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Data Analyst
Prime Connexar — June 2022–May 2023- Developed Python- and SQL-based data transformation workflows, improving data quality by ~35% and supporting downstream analytics and reporting.
- Built predictive models and KPI dashboards that improved customer retention by ~46% and accelerated response to business-critical events.
- Delivered clear, actionable visualizations that increased stakeholder adoption of analytics by ~57%.
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Education
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M.S. in Computer Science
Texas A&M University–Kingsville — December 2025Specialization: Cloud, DevOps, Data Engineering
Thesis: AWS Lambda cold start optimization using Redis
Coursework: Cloud Computing, Distributed Systems, Data Mining, Databases, Computer Networks -
Bachelor’s in Computer Engineering
Visvesvaraya Technological University — May 2023Foundations in DSA, OS, Databases, Software Engineering
Projects in web development, data visualization, and cloud fundamentals
Skills
Explore by clicking each category
Cloud Engineering
- AWS (EC2, S3, IAM, VPC, ALB, Lambda, CloudWatch)
- GCP (Compute Engine, BigQuery, IAM)
- Infrastructure as Code (Terraform)
- Networking & Security (VPC design, least-privilege IAM)
- CI/CD (GitHub Actions / pipelines)
- Docker, Kubernetes
- Observability & Monitoring (CloudWatch metrics, alarms)
- Automation & system reliability mindset
📁 Thesis & Projects
Reducing Cold Start Latency in Serverless Architectures
AWS Lambda • Redis • Performance Engineering
Overview
Master’s thesis focused on analyzing and mitigating cold start latency in serverless computing by evaluating caching strategies and architectural trade-offs in AWS Lambda environments.
Key Contributions
- Designed controlled experiments comparing no-cache, in-memory cache, and Redis-based external caching
- Measured cold start latency, warm start behavior, and execution overhead
- Analyzed performance vs. cost trade-offs under varying workloads
- Produced reproducible benchmarks and evaluation metrics
Tools & Technologies
- Serverless: AWS Lambda
- Caching: Redis (external cache)
- Cloud: AWS (IAM, VPC, CloudWatch)
- Language: Python
- Analysis: Benchmarking & performance profiling
What This Demonstrates
- Distributed systems performance analysis
- Cloud-native and serverless architecture design
- Experimental research & data-driven evaluation
- Cost-aware infrastructure optimization
Real-Time Financial Transactions Pipeline
GCP • Pub/Sub • Serverless • Analytics
Overview
Built a cloud-native, real-time data pipeline on Google Cloud Platform to simulate financial transactions, process events serverlessly, and deliver analytics-ready insights.
Key Features
- Real-time transaction ingestion using Pub/Sub
- Serverless data processing with Cloud Functions (Python)
- Automated transaction generation via Cloud Scheduler
- Analytics-ready storage in BigQuery
- Interactive dashboards built with Looker Studio
Tools & Technologies
- Messaging: Google Cloud Pub/Sub
- Serverless: Cloud Functions (Python)
- Data Warehouse: BigQuery
- Visualization: Looker Studio
- Automation: Cloud Scheduler
- Formats: Python, JSON
What This Demonstrates
- Real-time, event-driven data engineering
- Serverless pipeline design on GCP
- Analytics-first architecture and visualization
- Cloud-native scalability and cost efficiency
DoorDash Delivery Pipeline on AWS
Serverless • CI/CD • Event-Driven Architecture
Overview
Built a serverless, event-driven delivery pipeline simulating real-world DoorDash order processing with automated CI/CD deployment and cloud-native messaging.
Key Features
- Event-driven order ingestion using S3 triggers
- Serverless processing with AWS Lambda
- Real-time notifications using SNS
- Automated CI/CD pipeline with CodeBuild and CodePipeline
- Live simulation of food delivery order workflows
Tools & Technologies
- Serverless: AWS Lambda
- Storage: Amazon S3
- Messaging: AWS SNS
- CI/CD: AWS CodeBuild, CodePipeline
- Scripting: JSON, Bash
What This Demonstrates
- Event-driven system design
- Serverless application development
- CI/CD automation on AWS
- Scalable, cloud-native architecture
Cloud-Native CI/CD Pipeline on AWS
Docker • Terraform • ECS Fargate • GitHub Actions
Overview
Built a production-ready, containerized API deployed on AWS using Infrastructure as Code and a fully automated CI/CD pipeline.
Key Features
- Zero-downtime deployments with ECS rolling updates
- Automated build, test, and deploy on every commit
- Secure networking with ALB + private subnets
- Centralized logging and alerting
Tools & Technologies
- Cloud: AWS (ECS Fargate, ECR, ALB, VPC, IAM, CloudWatch)
- IaC: Terraform
- CI/CD: GitHub Actions
- Containers: Docker
- App: Python (FastAPI) / Node.js
What This Demonstrates
- Cloud-native architecture
- Infrastructure automation
- DevOps best practices
- Security & scalability fundamentals
SRE Monitoring & Reliability Engineering
Observability • Chaos Testing • Incident Response
Overview
Designed a reliability-focused monitoring system with real-time alerts, synthetic health checks, and automated service recovery.
Key Features
- Synthetic uptime monitoring using Route 53 health checks
- CloudWatch dashboards for latency, errors, and saturation
- Chaos testing by intentionally killing containers
- Automatic task replacement and service recovery
- Blameless postmortem and incident runbooks
Tools & Technologies
- Monitoring: Amazon CloudWatch
- Health Checks: Route 53
- Compute: ECS Fargate
- Automation: AWS CLI
- Docs: Runbooks & Postmortem templates
What This Demonstrates
- SRE mindset & operational ownership
- Incident handling & root cause analysis
- Reliability, observability, and resilience engineering
On-Prem to Cloud Migration (AWS)
Re-architecture • Cost Optimization • Scalability
Overview
Migrated a legacy monolithic application to a scalable, cloud-native AWS architecture with improved performance and reliability.
Key Features
- Re-architected monolith into containerized services
- Migrated storage to Amazon S3
- Introduced ALB for load balancing
- Implemented IAM least-privilege security
- Reduced infrastructure cost through autoscaling
Tools & Technologies
- Cloud: AWS (EC2, ECS, S3, ALB, IAM, VPC)
- Containers: Docker
- Networking: VPC, Security Groups
- Monitoring: CloudWatch
What This Demonstrates
- Real-world cloud migration experience
- Architecture modernization
- Cost-awareness & performance optimization
Serverless Event-Driven Pipeline (AWS)
Lambda • SNS • S3 • CloudWatch
Overview
Built an event-driven serverless pipeline for ingesting, processing, and routing data without managing servers.
Key Features
- Event-triggered Lambda functions
- Data ingestion via S3
- Asynchronous messaging with SNS
- Error handling and retries
- Centralized logging and alerts
Tools & Technologies
- Serverless: AWS Lambda
- Messaging: SNS
- Storage: S3
- Monitoring: CloudWatch
- Language: Python
What This Demonstrates
- Serverless architecture design
- Event-driven systems
- Cost-efficient cloud solutions
Fraud Detection & Customer Insights
IBM Cognos • Data Modeling • BI Dashboards
Overview
Designed and delivered interactive BI dashboards in IBM Cognos to analyze fraud patterns, customer behavior, and operational performance using structured financial datasets.
Key Features
- Built fraud detection KPIs to identify high-risk transactions
- Created customer insights dashboards to analyze trends and segments
- Developed performance metrics for operational monitoring
- Applied data cleansing and derived fields for accurate reporting
Tools & Technologies
- BI Platform: IBM Cognos Analytics
- Modeling: Data Modules, Derived Fields
- Visualization: KPIs, Heatmaps, Bar, Pie, and Line Charts
- Data Prep: Data modeling & cleansing
What This Demonstrates
- Business intelligence and executive reporting
- Fraud analytics and KPI-driven insights
- Data modeling and visualization best practices
✍️ Medium Blog Posts
Automating Daily Goal Reminders on AWS
Set up daily motivational reminders using AWS Lambda, EventBridge, S3, and SNS—all under free tier!
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Networking Essentials for DevOps
Break down VPCs, subnets, routing, and OSI layers through simple diagrams and AWS examples.
- 4
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- Read More
Setting Up Java Web App on AWS
Learn how I launched my first Java web application on AWS EC2 using Maven and Amazon Corretto 8.
- 8
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- Read More