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Data Cleansing Program Director

Today 2026/08/30
Other Business Support Services
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Job description

The Data Cleansing Program Director leads the Government-Wide data cleansing program — driving large-scale data quality uplift across multiple government entities. You will own the cleansing strategy, orchestrate a multi-vendor delivery model, oversee enterprise data modelling, and ensure that cleansed datasets are published into the Government-Wide catalogue and sharing platforms — sustainably, with strong governance, ongoing monitoring, and continuous improvement.


•      Define and lead the Government-Wide data cleansing strategy aligned with data quality standards, policies, and government priorities.


•      Establish a structured framework to prioritize critical datasets and drive phased execution across entities.


•      Standardize, automate, and scale the end-to-end data cleansing lifecycle — leveraging advanced tools and AI-driven solutions; govern reusable accelerators.


•      Establish and oversee a multi-vendor delivery model — coordinated execution, performance management, and scalability across entities.


•      Oversee large-scale data quality improvement initiatives aligned to defined quality dimensions, rules, and thresholds.


•      Oversee development of entities' enterprise data models — aligned with Government-Wide data standards, central information models, and interoperability requirements.


•      Ensure creation, standardization, and publication of cleansed datasets into the Government-Wide data catalogue and sharing platforms — enabling accessibility and reuse.


•      Oversee development of data quality dashboards to track KPIs and cleansing progress.


•      Establish and lead program governance bodies — driving timely, quality delivery, issue resolution, and stakeholder alignment.


•      Establish and govern sustainable data quality and cleansing frameworks for long-term maintenance and continuous improvement.


•      Provide regular reporting to senior leadership on program progress, risks, and impact on data maturity and value realization.


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