Scalable Multi Cloud Data Engineering with Airflow Orchestration and Iceberg Driven Governance

The rapid adoption of heterogeneous cloud ecosystems has created a growing need for scalable and fault tolerant data pipelines that operate consistently across distributed environments. Traditional ETL systems are often unable to maintain transactional integrity, metadata consistency, and performant analytics when extended across Azure, Google Cloud Platform, and on-premises sources. This paper presents a unified multicloud orchestration framework that combines AWS Managed Workflows for Apache Airflow, Apache Iceberg, and Amazon Redshift to deliver reproducible ingestion, ACID compliant refinement, and high performance analytical processing. The architecture leverages Iceberg's metadata model to enforce schema alignment and enable interoperable access across engines, while Airflow coordinates synchronization and incremental ingestion across clouds. Experimental evaluation using an 800 TB synthetic retail dataset demonstrates a 58% reduction in pipeline latency, improved storage efficiency, and elimination of schema related downtime. The results show that integrating open table formats with cloud native orchestration provides a scalable and cost optimized foundation for enterprise multi-cloud data engineering.