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Officeworks make informed, data-led decisions.</p><p><strong>Where you will make a difference:</strong></p><p><strong>In this role you will:</strong></p><p><strong>Data Quality Framework &amp; Rule Definition</strong></p><ul><li>Define and implement comprehensive data quality rules &amp; standards using Snowflake and specialised DQ tools.</li><li>Collaborate with business and engineering teams to identify critical data elements and quality requirements.</li><li>Establish automated native checks and validation logic within data pipelines.</li></ul><p>&nbsp;<strong>Monitoring &amp; Issue Resolution</strong></p><ul><li>Proactively monitor data quality across all pipelines and downstream systems to identify anomalies.</li><li>Investigate the root cause of data quality issues and work cross-functionally to drive remediation.</li><li>Support and maintain data quality tooling, such as Alation DQ, to ensure continuous oversight.</li></ul><p>&nbsp;<strong>Reporting &amp; Insights</strong></p><ul><li>Develop and maintain dashboards to report on key data quality metrics and trends.</li><li>Provide regular updates to stakeholders on the health of the enterprise data estate.</li><li>Track the progress of data cleansing activities and their impact on business outcomes.</li></ul><p>&nbsp;<strong>Continuous Improvement &amp; Innovation</strong></p><ul><li>Identify opportunities to automate manual data validation processes and enhance DQ maturity.</li><li>Stay abreast of emerging DQ methodologies, particularly as they relate to the leap from ML to AI.</li><li>Drive process improvements within the Data &amp; Analytics hub to streamline data operations and documentation standards.</li></ul><p>&nbsp;<strong>Collaboration &amp; Capability</strong></p><ul><li>Assist with broader data engineering or modelling tasks as required to support team delivery.</li><li>Contribute to the documentation of data lineage and metadata to ensure institutional knowledge is captured.</li></ul><p><strong>Who you will be working with:</strong></p><ul><li>Data &amp; AI Team: Collaborate with Data Architects, Modellers, and Engineers to embed quality at the source.</li><li>Enterprise Data Architect and Data Governance lead.&nbsp;</li><li>Analytics Teams: Partner with Data Scientists and Analysts to ensure high-quality training data for machine learning systems.</li><li>Business Stakeholders: Engage with functional partners to understand reporting requirements and resolve data discrepancies.</li><li>External Partners: Liaise with technology vendors and delivery partners to align quality standards across the ecosystem.</li></ul><p><strong>What success looks like:</strong></p><ul><li>Data Trust: Measurable improvement in data accuracy and reliability across the Snowflake platform.</li><li>Operational Efficiency: Reduction in the time taken to identify and resolve data quality incidents through automation.</li><li>Stakeholder Confidence: High levels of business 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