About the Service Line:
Capgemini Global Insights & Data business line is a market leader in Data Engineering, Cloud Data Platforms, Data Science, and AI and Advanced Analytics across all sectors including financial services, public sector, consumer products, telecommunication & energy resources. Our offerings include end-2-end data integration to cloud platforms, complete suite of data engineering capabilities and a category of sector-based advanced analytics and AI driven solutions.
This is a unique opportunity within the Insights & Data (I&D) Global Business Line (GBL) to help grow the data, AI and Data Science engagements across all sectors in Australia and New Zealand. You will help shape and deliver complex projects and programs of work for our clients. You will understand our client’s need to bring maximum value, impact, and innovation to the market as you oversee the delivery of client engagements for data and AI initiatives.
Let’s talk about the roles and responsibilities:
The Data Governance Lead is responsible for defining and leading the enterprise data governance strategy, establishing policies, standards, and processes to ensure high-quality, secure, and compliant data across the organisation. The role drives the adoption of governance frameworks, enabling trusted data for operational, analytical, and regulatory purposes.
This position acts as the bridge between business and technology, ensuring alignment of data governance initiatives with organisational strategy, regulatory requirements, and data-driven outcomes.
- Define and implement the enterprise data governance strategy aligned with business objectives and regulatory requirements.
- Establish governance frameworks, operating models, and stewardship structures across the organisation.
- Develop and maintain data governance policies, standards, and guidelines covering data quality, metadata, security, and lifecycle management.
- Define data quality frameworks, metrics, and monitoring processes to ensure data accuracy, completeness, and consistency.
- Define data ownership, stewardship, and accountability models across business domains.
- Establish governance bodies (e.g., Data Governance Council, Steering Committees) to oversee governance activities.
- Enable metadata-driven governance to improve data discoverability, transparency, and trust.
