Serverless computing, especially Function-as-a-Service (FaaS) platforms, enables developers to concentrate on software engineering without the burden of managing infrastructure. These platforms leverage stateless, event-driven functions, making them a good choice for workflows and function composition. Despite the advantages, serverless workflows can impose provider-specific constraints, resulting in portability issues and potential vendor lock-in. Previous work has tackled these limitations. QuickFaaS emphasized standardizing function definitions to create a consistent programming model. Building on this, OmniFlow introduced a Domain-Specific Language (DSL) that enables provider-agnostic serverless workflows, allowing seamless reuse across cloud environments.This ongoing work extends OmniFlow by enhancing serverless workflow execution and function composition. Key improvements include control flow-based execution for iterative tasks, and eliminating reliance on provider-specific constructs. Additionally, it introduces parallel execution, enabling workflows to scale efficiently by running independent tasks concurrently, improving performance, and reducing execution time. These enhancements enable more efficient execution of algorithms for big data processing tasks, including data ingestion, transformation, and analysis, by optimizing task distribution and parallel execution, allowing developers to focus on logic rather than infrastructure.