Business Analyst (Data Governance & AI Enablement)

We are seeking an experienced Business Analyst to support the development of data governance, data quality, and AI enablement capabilities for one of our clients. This is a foundational role in an organization moving from project-based delivery to a structured data strategy and governance operating model, with the mandate to lay the groundwork for a future data office. Key responsibilities • Data governance and strategy: Support the design and implementation of data governance frameworks, policies, and standards. Help define data ownership, stewardship, and accountability models, and partner with business and technology stakeholders to align governance practices across the organization. • Data quality management: Define and document data quality rules, metrics, and monitoring approaches. Identify gaps in data quality and support remediation strategies, while collaborating with delivery and operations teams to embed quality practices into day-to-day work. • AI use case assessment: Identify and evaluate AI and automation opportunities within business processes. Assess feasibility, risks, and data readiness for proposed AI initiatives, and partner with stakeholders to prioritize practical, high-value use cases. • Operating model development: Contribute to the creation of a data governance office structure and support the development of governance workflows and decision-making frameworks. Required qualifications • 4+ years of experience in Business Analysis, with demonstrated exposure to data, analytics, or governance initiatives • Strong understanding of data governance concepts, including ownership, lineage, stewardship, and controls • Experience with data quality analysis and issue resolution • Proven ability to work in ambiguous environments and drive structured, measurable outcomes Preferred qualifications • Familiarity with data governance frameworks or platforms such as Collibra, Alation, or DAMA • Exposure to AI/ML concepts and use case evaluation methodologies • Experience supporting organizational change or operating model design initiatives What success looks like • Clear, adopted data governance structures and processes are embedded across the organization • Improved data quality, visibility, and cross-functional accountability • Practical AI use cases identified, scoped, and advanced toward delivery • Foundations established for a scalable and sustainable data governance organization