Data Governance Specialist

Job Description

Data Quality : Define and Measure Data Quality Metrics: Establish metrics for accuracy, completeness, validity, consistency, timeliness, and reliability. Continuous Monitoring and Remediation: Regularly monitor data quality, conduct audits, perform root cause analysis for recurring data issues, and implement preventive measures and remediation plans. Data Profiling: Develop and maintain comprehensive data profiles to understand data characteristics. Data Validation: Create and implement validation rules to ensure that incoming data conforms to expected formats and values. Data Cleansing: Design and execute data cleansing processes to correct errors and inconsistencies, enhancing overall data quality and reliability. Data Governance : Establish Governance Framework: Implement and enforce data governance practices to ensure compliance with regulatory requirements and corporate policies, ensuring data is managed according to best practices. Metadata Management: Develop and maintain a comprehensive metadata repository to document data definitions, lineage, and usage, ensuring it is kept up to date and accessible to end users. Understand User Needs: Collaborate with business users to identify data needs, pain points, and requirements, ensuring the data is fit for its intended use. Identify Improvement Areas: Continuously seek opportunities for process improvement in data governance and quality management. User Roles and Access Requirements: Understand user roles and access requirements for systems, so that similar protection can be implemented into the analytical solutions. Row-Level Security: Work with the data & analytics team to establish row-level security for analytical solutions, ensuring data is accessible only to authorised users. Continuous Improvement: Establish Naming Conventions: Define business friendly table names and column names, along with synonyms, to ensure data easily accessible using AI. Create Synonyms: Implement synonyms to simplify data access and enhance data readability. Establish KPIs for data governance and quality efforts and create regular reports for stakeholders to track progress and demonstrate the value of data governance initiatives. Continuous Improvement: Establish a feedback loop where users can report data quality issues and suggest improvements.