The AI-Powered Multi-Cloud Data Engineer Course is designed to shape a new generation of data engineers who can operate at the intersection of cloud computing, artificial intelligence, large-scale data systems, and enterprise automation. As organizations evolve into data-driven and AI-first ecosystems, the demand has shifted from traditional pipeline developers to engineers capable of orchestrating intelligent, self-healing, and cross-cloud data architectures.

This course envisions creating professionals who not only understand the complexities of AWS, Azure, and Google Cloud independently but can also design seamless, interoperable multi-cloud data platforms that support mission-critical business decisions and large-scale AI workloads.

Learners will be guided from foundational concepts to highly advanced topics such as AI-driven ETL generation, metadata automation, vectorized data processing, cloud-neutral governance, and intelligent orchestration frameworks. By blending real-world enterprise use cases, AI-assisted engineering, and hands-on cloud labs, this program ensures participants develop a deep, practical skillset aligned with current industry expectations.

By the end of this course, every learner will not only understand how to build robust data systems—but how to create adaptive, scalable, and intelligent data ecosystems that evolve automatically using ML models, LLM agents, and cloud-native automation. The vision is to empower professionals to become true AI-native, multi-cloud architects capable of shaping the future of data engineering.
Outcome: Certified Multi-Cloud AI Data Engineer

Module 1: Foundations of Modern Data Engineering

• What is Multi-Cloud Data Engineering?
• Traditional vs. AI-Accelerated data engineering.
• Data lifecycle, Python, SQL, APIs, CI/CD.
• LLMs in future data engineering.
  Hands-On: Build AI-assisted ETL workflow.

Module 2: Multi-Cloud Architecture – AWS, Azure & GCP

• Cloud-neutral architecture, networking, identity.
• Storage systems, cross-cloud transfers.
• Vendor lock-in mitigation.Hands-On: Deploy unified multi-cloud data lake.

Module 3: AI-Augmented Data Ingestion & ETL/ELT

• Intelligent, AI-generated ingestion and ETL logic.
• Kafka, Kinesis, EventHub, Pub/Sub.
• AI-driven validation and lineage.Hands-On: Build AI-generated ETL pipeline.

Module 4: Multi-Cloud Warehousing & Lakehouse Engineering

• Lakehouse vs Warehouse.
• Snowflake, Databricks, BigQuery, Redshift, Synapse.Hands-On: Build cross-cloud lakehouse using Delta Lake.

Module 5: Machine Learning for Data Engineers

• ML models every engineer needs.
• AI for pipeline optimization.Hands-On: Deploy ML-powered anomaly detector.

Module 6: Security, Governance & Compliance

• Zero-trust, IAM, encryption.
• AI-based PII detection and governance.Hands-On: AI-powered sensitive data tagging.

Module 7: Orchestration & Automation

• Airflow, Prefect, Dagster.
• Serverless data pipelines.Hands-On: Multi-cloud Airflow DAG with LLM logic.

Module 8: Real-Time & Streaming Systems

• Kafka, Flink, Spark Streaming.
• AI-powered real-time enrichment.Hands-On: Build semantic streaming pipeline.

Module 9: Generative AI Integration for Data Engineers

• Prompt engineering for SQL/Python.
• Vector databases, LangChain, DSPy.Hands-On: Build vector-augmented ETL.

Module 10: Capstone Project

• Deploy multi-cloud lakehouse.
• AI-powered pipelines.
• ML-based monitoring.
• GenAI-driven metadata extraction.

🎓 What Learners Achieve

By the end of this course, learners will become experts in:

• Multi-cloud data architecture & orchestration
• AI-powered ETL design and automation
• Real-time streaming & batch processing
• ML-assisted data quality, lineage & governance
• GenAI-enabled transformation and analytics
• End-to-end enterprise-scale data engineering implementation