In modern data work, the gap between development and production environments presents significant challenges, often causing data pipelines that run smoothly in development to fail in production due to variations in data volumes, schemas, or system dependencies. Continuous Integration and Continuous Delivery (CI/CD) practices address these issues by implementing structured approaches that include automated testing, deployment, and monitoring processes. This enables data engineers to detect issues earlier, deploy with greater confidence, and adapt quickly to changing requirements. CI/CD originated from software development to streamline integration and reduce conflicts between developers' work, and it extends to Continuous Delivery and Continuous Deployment, where code is kept in a deployable state and can be automatically deployed to production. In data engineering, CI/CD must accommodate large volumes of stateful data, changing schemas, and data quality issues, requiring isolated environments for development, testing, and production to prevent test operations from affecting production data. The practice of CI/CD encourages modular pipeline architecture, allowing for scalability, adaptability, and testability, and involves strategies such as "shift left" for early testing during development and "shift right" for operational resilience post-deployment. This approach leads to a continuous feedback loop, improving pipeline reliability and allowing teams to deploy changes with confidence. Implementing CI/CD is a gradual process, starting with basic automation and expanding as teams gain experience, supported by platforms like Jenkins, GitHub Actions, and GitLab CI. The successful adoption of CI/CD depends on cultural shifts within teams towards frequent integration, comprehensive testing, and automation, ultimately resulting in less stress, quicker value delivery, and improved system reliability.