You're interested in data engineering. How can you develop your data governance skills?

To understand data governance frameworks easily, start by focusing on their core purpose: managing data effectively within an organization to meet business goals and comply with regulations. Begin with foundational frameworks like DAMA-DMBOK for comprehensive data management practices, COBIT for IT governance linking to business objectives, and ISO 38500 for guiding corporate governance of information technology. Utilize resources like introductory guides, online courses, and summary articles to grasp basic principles. Apply these concepts to hypothetical or real-life data projects to see how they influence data strategy, compliance, and culture.

1.Understand the Principles of Data Governance 2.Collaborate with Data Governance Teams 3.Implement Data Quality Measures 4.Implement Data Security Measures 5.Monitor and Audit Data Governance Processes

Understand the basic concepts and principles of data governance frameworks: Start by familiarizing yourself with the fundamental concepts and principles of data governance. This includes understanding the importance of data quality, data privacy, data security, and compliance with regulations.

To check data accuracy, compare sample data entries against a trusted source, like comparing addresses in your database against a postal service database. For completeness, generate a report highlighting missing values in critical fields, such as blank email addresses in a customer contact list. To ensure consistency, review datasets for uniformity in data formats, such as ensuring all dates follow the same format (YYYY-MM-DD) across different data tables or systems. These examples illustrate practical ways to assess and improve data quality.

From my own work with the Databricks Unity Catalog ... you should consider the following: Learn about data security: use Databricks articles to understand concepts such as encryption, access control, and data anonymization to protect sensitive information. Explore the Unity Catalog: Use this tool in a data engineering project to organize and manage metadata to ensure data sequencing and compliance. Study best practices: Read Databricks' blog articles on data governance to understand industry standards and strategies for effective governance implementation. Practice using Databricks features: Experiment with features such as fine-grained access controls and audit logging to enforce governance policies in data pipelines and workflows.

Apply privacy and security in data engineering; you must understand the basic principles of security and privacy; not just the tool! The tool help you to implement, but you must know the fundamental that support the solution that you will implement based in scenario and context: this will help to get more idea to madurity your pipeline. Learn about CIA triade, space vulnerability, Data classification, data inventory, data subject request like accessing or deleting, encription, anomynization, etc.

Data governance experts and peers are people who have extensive experience and knowledge in data governance, and can offer you insights, advice, feedback, and guidance. Learning from data governance experts and peers can involve joining data governance communities, forums, or groups, such as Data Governance Professionals Organization, Data Governance Society, or Data Governance Network, where you can network, share, and learn from others.

To develop data governance skills as a data engineer, start by understanding data governance principles and frameworks. Learn about tools for metadata management, data quality, and access control. Get practical experience by collaborating on data governance initiatives within your organization and stay updated with continuous learning through training courses and workshops.

Practicing data governance skills and implementing data quality measures is crucial for ensuring that an organization's data is accurate, reliable, and compliant with regulations. Understanding Data Lifecycle: Gain a comprehensive understanding of the data lifecycle, including data acquisition, storage, processing, and archival. This knowledge helps in identifying critical stages where governance measures can be implemented. b. Regulatory Compliance: Familiarize yourself with data privacy regulations such as GDPR, CCPA, or industry-specific compliance standards. Understand the requirements for data handling, consent management, and data subject rights to ensure data governance aligns with legal obligations.