Ewelie Joseph Ebuka

Cloud Engineer

Project: The Future of Tech in Real Estate

This project reimagines property management and development through cutting-edge cloud infrastructure. By leveraging scalable solutions and data analytics, it aims to create intelligent, secure, and highly efficient platforms that transform how real estate businesses operate and interact with their assets.

About Ewelie Joseph Ebuka

Professional Bio

Ewelie Joseph Ebuka is a dedicated and innovative Cloud Engineer with a passion for building robust, scalable, and secure cloud solutions. With a strong background in AWS, Azure, and Google Cloud Platform, Ewelie excels at designing, deploying, and managing cloud-native applications and infrastructure. Their expertise spans across various cloud services, including serverless computing, containerization, CI/CD pipelines, and infrastructure as code.

Education

  • B.Sc. Computer Science, University of Lagos
  • Certifications: AWS Certified Solutions Architect - Professional, Azure Solutions Architect Expert, Google Cloud Professional Cloud Architect

Skills

AWS Azure GCP Docker Kubernetes Jenkins Terraform Python CI/CD DevOps

Past Experiences

  • Senior Cloud Engineer at InnovateTech Solutions

    Led the migration of on-premise infrastructure to AWS, reducing operational costs by 30%.

  • Cloud Consultant at NextGen Cloud Services

    Designed and implemented scalable cloud architectures for various enterprise clients.

  • DevOps Engineer at CloudBurst Corp

    Developed and maintained CI/CD pipelines, improving deployment frequency by 50%.

Past Projects

  • Automated Cloud Cost Optimization Tool

    Developed a Python-based tool that uses AWS APIs to identify and optimize underutilized cloud resources, saving significant costs for clients.

  • Containerized Microservices Platform

    Designed and deployed a highly available microservices architecture using Kubernetes on Azure, ensuring seamless scalability and resilience.

  • Serverless Data Processing Pipeline

    Implemented a real-time data ingestion and processing pipeline using AWS Lambda, Kinesis, and S3 for a large-scale analytics project.