This is AllonData Academy's flagship learning path.
Instead of learning cloud services individually, you build one complete production-ready Data Engineering platform from scratch, with every service taught within the context of a real engineering project.
Curriculum
What you'll learn
Data Platform
You'll start with the foundational Google Cloud building blocks the rest of this path builds on: storage, a query engine, access control, and a project structure that holds up as it grows.
- Cloud Storage
- BigQuery
- IAM
- Project structure
Data Processing
You'll use serverless compute to process data without managing a single server, the same building blocks production pipelines run on.
- Cloud Run
- Cloud Functions
Workflow Orchestration
You'll learn to automate and sequence the steps of a pipeline, so jobs run in the right order on their own instead of someone kicking them off by hand.
- Google Cloud Workflows
- Cloud Scheduler
Event-Driven Architecture
You'll build systems that react the moment something happens instead of polling to check, the way modern production pipelines are designed.
- Pub/Sub
- Eventarc
Engineering Practices
The professional practices that turn a script into a production system: version control, CI/CD, infrastructure as code, and the habits that keep a platform maintainable long after launch.
- Git
- GitHub
- CI/CD
- Terraform fundamentals
- Infrastructure as Code concepts
- Logging
- Monitoring
- Documentation
- Deployment strategy
- Code reviews
Python
Python is used throughout this path for building cloud-native Data Engineering solutions on Google Cloud. This isn't a Python programming course: you'll be guided on the essential concepts needed to understand modern Data Engineering codebases, clear technical interviews, and follow the hands-on project confidently, with a structured self-learning roadmap focused only on what's relevant here. Development is done with AI-assisted tools, and you'll learn to review, modify, debug, and maintain AI-generated Python code as you build production-style solutions.
AI-Assisted Development
Modern engineering teams increasingly use AI-assisted development. Throughout the program, you'll use tools like Visual Studio Code Copilot and other AI coding assistants to accelerate development.
- Responsible AI-assisted development
- Reviewing generated code
- Debugging AI-generated solutions
- Improving generated implementations
- Writing effective engineering prompts
- Maintaining production-quality code
- Using AI to accelerate Python development while understanding every implementation
Who can learn
Open to fresh graduates, working professionals, career switchers, software engineers, cloud engineers, and data analysts, especially those who've completed BigQuery & Analytics Engineering or have equivalent BigQuery knowledge.
Prerequisites
SQL fundamentals
Basic BigQuery knowledge
Basic Python understanding is recommended. If you don't have prior Python knowledge, you'll be guided on the essential concepts needed, along with a structured self-learning roadmap. This isn't a full Python programming course.
Capstone Project
You build one complete production-style Google Cloud Data Engineering project, integrating every technology covered in the path into a practical, end-to-end implementation.
You'll experience how real-world Google Cloud Data Engineering systems are designed, developed, deployed, and maintained, working throughout with professional engineering practices: sprint planning, architecture discussions, code reviews, Git workflow, documentation, deployment, debugging, and production best practices.
Integrated Engineering Internship
Delivered as an Integrated Training + Internship Program: you participate in structured engineering activities under mentor supervision while building production-style solutions.
On successful completion, you receive an Internship Certificate, a mentor evaluation, a production project portfolio, and career guidance.
Hiring Network Interview Opportunities
AllonData collaborates with hiring managers and recruiters who are open to interviewing candidates with strong practical skills.
If you complete the learning path and production project, demonstrate production-ready engineering skills, and pass AllonData's internal technical assessment, you may be recommended for interview opportunities through AllonData's hiring network.
Interview opportunities are merit-based and subject to hiring demand. Final hiring decisions are made solely by the recruiting companies.
Learning outcomes
After completing this path, you'll be able to:
- Design cloud-native Google Cloud Data Engineering architectures
- Build complete ETL and ELT pipelines
- Develop event-driven data engineering solutions
- Orchestrate production workflows using Google Cloud services
- Build production-ready Google Cloud applications
- Read, understand, modify, debug, and maintain Python code used in Data Engineering projects
- Apply Infrastructure as Code concepts using Terraform
- Use AI-assisted development responsibly in production environments
- Collaborate using modern engineering practices
- Contribute confidently to professional Google Cloud Data Engineering teams
What's included
- Live interactive training
- Mentor support
- Weekly engineering reviews
- Architecture reviews
- Mock technical interviews
- Resume review
- LinkedIn profile review
Ready to start with Google Cloud Data Engineering?
Tell us a bit about where you are, and we'll help you get started.