AI-Powered Cloud Data Engineering - Azure & Databricks
Build the Cloud Data Platforms That Power Modern AI, Analytics, and Business Intelligence.
Why Learn AI-Powered Cloud Data Engineering?
AI-Powered Cloud Data Engineering combines cloud computing, data engineering, analytics engineering, and AI-assisted development to prepare professionals who can build the modern data infrastructure organizations rely on for AI, business intelligence, and digital transformation.
Career opportunities upon graduation
This program will prepare you with skill and experience to be competent and competitive in the following positions:
Who Should Take This Course?
This course is designed for professionals and graduates seeking to build or elevate their enterprise data analytics capabilities.
- Data Analysts
- Business Analysts
- IT Professionals
- Cloud Administrators
- Software Developers
- Recent University Graduates
- Career Changers
- Database Professionals
What You Will Learn
The course is organized into three progressive subjects. Throughout the course, you will build one continuous enterprise cloud data engineering project that demonstrates your practical skills and becomes a professional portfolio.
Subject 1 – Foundations of AI-Assisted Cloud Data Engineering
Build core cloud data engineering and AI-assisted pipeline skills. Set up Azure and Databricks, ingest data, design the bronze layer, and validate data quality for the Ontario financial services project. Complete Phase I with a technical report that becomes the foundation for Subject 2.
Subject 2 – Advanced AI Cloud Pipelines & Automation
Transform the Phase I bronze layer into silver and gold layers using PySpark and dbt. Orchestrate the pipeline with Airflow, connect curated data to Power BI, and add data-quality tests. Complete Phase II with a technical report and a live pipeline demonstration.
Subject 3 – AI-Powered Cloud Data Engineering Project
Refine the Phase II pipeline into a production-style enterprise cloud data platform. Optimize, validate, document, and present the completed solution while applying governance and operational practices through guided templates and exercises. Finish with portfolio-ready technical documentation and a final presentation.
Build an Enterprise Project
Learning goes beyond theory. Throughout the program, you'll build one continuous enterprise cloud data engineering project that reflects how modern organizations manage data for analytics, business intelligence, and Artificial Intelligence. Using Microsoft Azure, Azure Data Factory, Databricks, Apache Spark, Delta Lake, dbt, Airflow, Power BI, and AI-assisted development tools, you'll progressively design, build, automate, optimize, and present a production-style cloud data platform.
Throughout the project, you'll gain hands-on experience with:
- Cloud Data Pipelines
- Data Lake Architecture
- Lakehouse Design
- ETL / ELT Workflows
- PySpark Transformations
- Data Quality Frameworks
- BI Dashboards
- Enterprise Documentation
Admission Requirements:
1. University degree in any field, or equivalent professional experience in a data-adjacent role (analyst, developer, IT professional)
2. Able to use at least one analytical tool or language (SQL, Python, R, Excel, or Power BI).
3. Pass an admission interview.
Why Study at Metro College?
For more than 25 years, Metro College has been helping students develop the practical skills employers are looking for. Our programs are designed in collaboration with industry trends and emphasize hands-on learning, real-world projects, and career readiness—so you graduate prepared to contribute from day one.
- Career-Focused Learning
- One Continuous Project
- Live Instructor-Led Delivery
- Small class sizes
- AI with Validation Discipline
- Flexible Delivery
- Azure & Databricks Experience
- Flexible Delivery
Selected Employers That Have Hired Metro SAS Graduates
AI-Powered Cloud Data Engineering Certification
After completing the program and gaining additional self-study or hands-on experience where required, students may choose to pursue certifications such as:
Microsoft Certified: Azure Data Engineer Associate (DP-203)
Microsoft Certified: Azure Fundamentals (AZ-900)
Microsoft Certified: Azure AI Fundamentals (AI-900)
Databricks Certified Data Engineer Associate
Microsoft Certified: Power BI Data Analyst Associate (PL-300)
If you want to know more about the Microsoft Certified exam, please visit Microsoft Certified: Azure Data Fundamentals.
Frequently Asked Questions
Find answers to common questions about the Applied AI Systems - ML and LLM Engineering course.
Rather than focusing only on theory, this program teaches students how to build, deploy, monitor, and govern complete AI systems using machine learning and LLMs.
Yes. Students develop one continuous enterprise AI project and graduate with a GitHub-hosted portfolio demonstrating production-ready AI engineering skills.
It is the practice of designing, building, automating, and managing cloud-based data platforms that support analytics, business intelligence, and AI applications.
Students gain hands-on experience with Microsoft Azure, Azure Data Factory, Azure Data Lake Storage Gen2, Databricks, Apache Spark, dbt, Airflow, and Power BI.
Graduates may work as Applied AI Engineers, Machine Learning Engineers, Senior Data Analysts, Data Scientists, AI Systems Analysts, LLM Applications Developers, and related AI professionals.
If you are interested in a career as data analyst or data scientist, please visit our Data Science and Artificial Intelligence program.
For learn more about other information technology courses, please visit the following subject pages: