We are looking for a skilled Data Engineer with hands-on experience in workflow orchestration, data integration, and cloud-based big data platforms. The role focuses on migrating and re-platforming existing integration workflows, building reliable ETL pipelines, and contributing to Lakehouse-style data architectures that support both batch and streaming workloads. The ideal candidate has practical experience working with integration platforms, structured data ingestion, and distributed data processing frameworks, and is comfortable operating in non-trivial enterprise environments with multiple applications, data volumes, and operational constraints. Key Responsibilities Integration & Workflow Engineering Analyse and re-platform existing workflow-based integrations from one platform to another while maintaining functional parity. Implement orchestration logic including triggers, conditional routing, retries, exception handling, and state management. Design and implement automation patterns to handle platform limitations (e.g., looping, batching, pagination, throttling). Ensure workflows are idempotent, fault tolerant. Support environment-based deployments (DEV / UAT / PROD) with configuration-driven designs. Data Engineering & ETL Design and build ETL pipelines for ingesting flat files (e.g., CSV) into relational databases. Handle schema validation, basic schema evolution, data quality checks, and error reconciliation. Optimize data ingestion for performance, scalability, and reliability. Collaborate with application teams to understand upstream and downstream data dependencies. Cloud & Big Data Platform Contributions Build and maintain data pipelines on cloud-based big data platforms using distributed processing frameworks. Contribute to Lakehouse-style data storage that supports both batch and streaming data. Work with modern table formats that support incremental processing, versioning, and historical queries. Support use cases such as append-heavy datasets, high-