Beyond ETL and DWH: The Integral Contribution of Data Quality Engineers in Data Governance
Beyond ETL and DWH: The Integral Contribution of Data Quality Engineers in Data Governance
Role of Data Quality Engineering in Modern Data Governance.
Recently, I received an intriguing question via a LinkedIn message that has been gaining significant attention in the data field: ‘ What are Data Quality Engineer roles in Data Governance?’ This question not only piqued my curiosity but also highlighted a key area of discussion among data professionals. In this article, I want to bring up this topic for discussion and also I aim to share my insights on this.
Data Governance is indeed related to ETL, DWH, and BI but is also a distinct policy in a broader sense of data management.
As we know, the ETL, DWH, and BI are essentially for organizing and making sense of the larger data for informed decision-making, on the other hand, Data Governance is essentially more about the overall management of the availability, usability, integrity, and security of the data in an organization and it includes setting policies (by collaborating with other stakeholders/departments/roles), standards, procedures to ensure that the data is well-managed and in compliance with business requirements and regulations.
In my understanding, data governance has more responsibilities than just ETL and DWH because it involves strategic planning for data management, data QA, data security, and compliance with legal requirements. Data governance ensures consistency and trustworthiness of the data across the organization.
As mentioned in the question already “Data Quality Engineer in ETL BI Big Data validates the Data by SQL/HQL”, the role of data QA in data governance is slightly different as the focus here extends beyond technical validations which means that the Data Quality Engineer here within the context of Data Governance might be involved in close collaboration with other departments or the stakeholders in implementing the data quality standards and policies, understanding and/or gathering the data requirements, monitoring the defined quality metrics for maintaining and improving the data standards. The data quality engineer might also be involved in ensuring the data quality issues are identified and documented as well as being resolved on time etc.
If you’re interested in more content about Software QA & Data QA, be sure to Subscribe.
Medium: https://medium.com/@ahsan924
LinkedIn: https://www.linkedin.com/in/ahsanbilal/