To build reusable data engineering components in Databricks, focus on modular design by creating testable Python/Scala libraries instead of relying on %run notebooks. Parameterize all notebooks using widgets for dynamic execution across environments. Leverage Delta Lake and Unity Catalog for consistent data governance and shared access across pipelines. Implement rigorous version control using Databricks Repos and Git, backed by a CI/CD process that automates testing, builds library artefacts, and deploys job configurations. This approach standardizes data transformation logic and improves collaboration and pipeline resilience.