Developed a unified Multi-source Data Access Template for the Sema4AI/actions repository, enabling seamless integration of both file-based and PostgreSQL data sources. Leveraging Python and SQL, the solution introduced reusable templates and configuration files that streamline data access and accelerate onboarding for analytics teams. Example queries, such as fetching customer information by country and computing monthly sales per company, were provided alongside sample data to facilitate adoption. The work established a flexible foundation for cross-source data engineering workflows, improved code quality, and promoted rapid reuse. This approach addressed the need for consistent, efficient data access patterns across diverse data environments.