Purpose

The Data & AI Manager is responsible for defining and executing EIA’s data, analytics, and artificial intelligence agenda to

enable data-driven decision-making across investment and corporate functions.

Operating within a sovereign wealth fund context, the role focuses on building scalable data platforms, advanced

analytics capabilities, and AI use cases that support investment performance, risk management, strategic planning, and

operational efficiency, while ensuring strong governance, data quality, and responsible AI use. The role focuses on AI and

analytics value creation, prioritisation, and adoption, working in partnership with IT for platform delivery and operations.

Strategic Responsibilities

• Define and lead EIA’s Data & AI Strategy, ensuring alignment with the Digital Strategy, investment priorities,

and corporate strategy.

• Establish enterprise-wide data governance frameworks, including data quality standards, ownership models,

and analytics principles.

• Identify, prioritize, and sequence AI and advanced analytics use cases that deliver measurable business value.

• Promote data literacy and adoption across investment and corporate teams to embed data-driven decisionmaking.

• Act as a strategic partner to senior leadership on data, analytics, and AI-enabled insights.

Core Responsibilities

1. Data Architecture & Analytics Platforms

• Define data and analytics requirements, standards, and target‑state architecture in collaboration with IT and

enterprise architecture.

• Ensure data and AI solutions meet business requirements for scalability, security, and governance, in

coordination with IT.

• Oversee data ingestion, transformation, and modelling to support analytics and reporting needs, with technical

data pipeline development and engineering owned by IT.

2. Analytics, AI & Decision Support

• Own the end‑to‑end AI use‑case lifecycle, including identification, prioritisation, business case development, value measurement, and adoption.

• Develop dashboards, predictive models, and decision-support tools for investment, strategy, risk, and performance teams.

• Lead the delivery of AI-enabled solutions, including advanced analytics, forecasting, and pattern detection.

• Ensure AI use cases are aligned with business objectives and supported by reliable data foundations.

• Technical build and platform operations are delivered jointly with IT and external partners.

3. Data Governance & Quality

• Ensure data quality, lineage, consistency, and transparency across enterprise systems.

• Implement data standards, controls, and monitoring mechanisms to support governance and audit requirements.

• Support responsible and ethical use of AI, including transparency, explainability, and appropriate human oversight.

• Develop AI acceptable‑use guidelines, human‑in‑the‑loop principles, and AI literacy programmes in partnership with Risk, Compliance, and HR.

4. Stakeholder Collaboration & Enablement

• Partner with IT and business stakeholders to implement analytics and AI solutions.

• Translate business needs into data and AI requirements and ensure solutions are adopted effectively.

• Support strategic initiatives through advanced analytics and insight generation.

People & Capability Responsibilities

• Build and develop data and analytics capabilities across teams.

• Establish knowledge-sharing practices, standards, and best practices for analytics and AI adoption.

• Support capability uplift through training, tools, and data literacy initiatives.

Key Deliverables

• Enterprise data platforms and standardized analytics dashboards.

• AI use cases supporting investment research, risk analysis, performance measurement, and strategic planning.

• Data governance frameworks, policies, and standards.

• Insight-driven support for strategic and investment initiatives.