Data Analyst

The RoleReporting to the Global Manager, Data Access Control, the Data Access Control Analyst is the driver of all technical aspects of the Data Governance team. The Data Access Analyst will apply all rules and associated policies and procedures of the Data Governance process to all user and data access requests. The Data Access Analyst should be customer focused and impacts organisational performance on processes related to client data. This individual should apply their technical skills to execute the Data Governance framework and associated policies and procedures. The individual will also be building ETL pipelines and developing dashboards for auditing and analytical purposes to ensure our data remains governed. Key ResponsibilitiesData Engineering & ETL: Build and automate robust data pipelines in Domo using Magic ETL and Python. You’ll integrate and consolidate metadata from multiple instances to ensure high-quality data accessibility for reporting and auditing..Advanced Visualization: Design, build, and maintain sophisticated dashboards in Domo, Power BI, or Tableau that provide /"at-a-glance/" clarity for executive stakeholders.Data Governance: Act as a steward for data integrity. You’ll implement governance standards to ensure data consistency, security, and accuracy across the organization.Strategic Analysis: Perform deep-dive investigations into trends and variances, turning raw numbers into narratives that support our long-term strategic initiatives.Modernization & Mentorship: Lead the shift toward automated reporting. You’ll educate junior team members on new tools and best practices in Python and BI.What You BringTechnical Excellence: Strong proficiency in Python (Pandas, NumPy) and advanced SQL.BI Mastery: Extensive experience with at least one major BI tool (Domo, Power BI, or Tableau).Analytical Mindset: You can solve complex problems independently by critical thinking and are comfortable navigating /"grey areas/" where precedents might not exist.Governance Exposure: A solid understanding of data lineage, metadata management, and data quality frameworks.Data Quality:Monitor and track data quality issues, identify gaps, and apply action plans to remediate gapCommunication: The ability to explain complex technical insights to non-technical audiences and influence decision-making.

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