Hi, I'm Moein. I help organizations build scalable cloud infrastructure and robust data solutions.

My work focuses on designing and implementing cloud-native architectures that scale: from data pipelines to machine learning infrastructure, I build systems that turn raw data into actionable insights.

This expertise has led me to work with organizations across various industries, helping them migrate to the cloud, optimize their data infrastructure, and implement best practices for cloud security and cost management. I specialize in AWS, Azure, and GCP, with deep experience in building ETL pipelines, data warehouses, and real-time streaming solutions.

At a personal level, I'm passionate about teaching and mentoring. I believe in making complex cloud concepts accessible to everyone, from beginners to seasoned professionals.

I regularly share my knowledge through blog posts and technical tutorials. I also conduct workshops and training sessions on cloud architecture, data engineering, and DevOps best practices.

Please get in touch if you're interested in collaboration, consulting, or speaking engagements. I'm always excited to work on challenging projects at the intersection of cloud architecture, data engineering, and education.


logo

Experience

Cloud Solution Architect (current)

Data Engineer

DevOps Engineer

Software Developer

Skills

AWS, Azure, GCP

Kubernetes, Docker, Terraform

Python, SQL, Spark

Airflow, dbt, Kafka

CI/CD, Git, Jenkins

Education

MSc in Computer Science

BSc in Software Engineering

AWS Certified Solutions Architect

Azure Certified Data Engineer

Projects

Data Pipeline Framework, open-source ETL toolkit

Cloud Cost Optimizer, infrastructure cost management tool

Real-time Analytics Platform, streaming data solution

Let's connect

LinkedIn

GitHub

Twitter/X

Instagram

Misc.

I'm currently working on a comprehensive cloud architecture course. I also contribute to open-source projects and speak at tech conferences about cloud and data engineering topics.



Copy-paste ready snippets for event organizers and media :)


Biography

Moein Ghaeini is a Cloud Solution Architect and Data Engineer with extensive experience in designing and implementing scalable cloud infrastructure and data solutions. He specializes in helping organizations migrate to the cloud, optimize their data pipelines, and implement best practices for cloud security and cost management. Moein is passionate about education and regularly conducts workshops and training sessions on cloud architecture, data engineering, and DevOps best practices. He holds an MSc in Computer Science and is certified in AWS Solutions Architecture and Azure Data Engineering.


Public Speaking

Moein has delivered talks and workshops at various tech conferences and meetups, covering topics such as cloud architecture patterns, data engineering best practices, and infrastructure as code. He enjoys breaking down complex technical concepts into accessible insights for audiences ranging from developers to CTOs. His speaking engagements include presentations on cloud migration strategies, building real-time data pipelines, and implementing cost-effective cloud solutions.


Expertise

Moein's technical expertise spans cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), infrastructure as code (Terraform, CloudFormation), data engineering (Airflow, dbt, Spark), and DevOps practices (CI/CD, GitOps). He has architected solutions for data-intensive applications, machine learning infrastructure, and enterprise-scale cloud migrations. His approach emphasizes security, scalability, and cost optimization in every project.


Featured Projects

  • Data Pipeline Framework - An open-source ETL toolkit that simplifies building and maintaining data pipelines across multiple cloud platforms.
  • Cloud Cost Optimizer - Infrastructure cost management tool that helps organizations reduce cloud spending by up to 40% through automated resource optimization.
  • Real-time Analytics Platform - Streaming data solution processing millions of events per day with sub-second latency for real-time business intelligence.
  • ML Infrastructure Suite - End-to-end machine learning infrastructure supporting model training, deployment, and monitoring at scale.

→ View all projects on GitHub

Top