• Project leadership: Independently plan and execute MDM projects with a focus on quality, budget and timelines. • Strategy and design: Analyze requirements, develop viable concepts and prepare management decisions. • Process design: Design, implement and optimize processes to ensure data quality and improve efficiency. • Governance: Define roles, responsibilities and structures for effective data governance. • Data analytics & modelling: Use data analysis to optimize master data models and support data-driven business decisions. • Client advisory: Consult and engage our clients with innovative Master Data Management solutions.

• Assess current cyber controls against defined frameworks (e.g. NIST) and organisational standards • Identify gaps between internal controls and external benchmark requirements (e.g. Tier 1 Bank aligned KRIs) • Define and implement control improvements to close identified gaps • Measure and track performance against Key Risk Indicators (KRIs) • Work closely with senior stakeholders to align on risk posture and remediation priorities • Drive adoption of new control frameworks across the business • Ensure strong documentation, governance, and audit readiness • Support automation of controls and reporting where possible • Deliver scalable, sustainable control improvements

• Own the end-to-end Data Quality program: SLAs, monitoring, testing, issue resolution and continuous improvement. • Drive requirements gathering with business and data teams to define and document technical and business data quality rules. • Build and maintain dbt-based Data Quality tests across the medallion architecture (Bronze / Silver / Gold layers). • Build DQ dashboards and lead root-cause analysis to improve trust in data. • Build and maintain the enterprise data catalog and business glossary, ensuring metadata, ownership, lineage, and business definitions remain accurate, documented, and discoverable. • Partner with domain teams to capture institutional knowledge and translate business context into shared metadata, glossary definitions, classifications, and governed semantic context. • Enable trusted and reusable data foundations for analytics and AI use cases by improving discoverability, context, and governance of data assets. • Support governance and compliance practices for sensitive and regulated data. • Act as the bridge between Data and Business teams on definitions, ownership, trust, and governance decisions. • Facilitate workshops with domain owners to define business rules and data contracts. • Drive data discoverability and become the trusted point of contact for data meaning and reliability.

• The Data Governance Lead supports GH engagements by designing and delivering enterprise‑level data governance capabilities. • This role partners with clients and technical teams to ensure data is well‑governed, trusted, and ready to support analytics, AI, and operational platforms. • Lead data governance strategy, frameworks, and operating models across client engagements • Own governance domains including data quality, metadata, lineage, stewardship, and lifecycle management • Guide implementation and adoption of data catalogs and governance tooling • Partner with data engineering, analytics, and AI teams to embed governance into technical solutions • Support data privacy, compliance, and risk management requirements • Manage governance workstreams, deliverables, and client stakeholders • Contribute to proposals, solution shaping, and practice knowledge sharing • Mentor junior team members and support internal governance communities of practice