Lead, Data & AI Governance (SG)

About Us

Headquartered in Singapore, SATS Ltd. is one of the world’s largest providers of air cargo handling services and Asia’s leading airline caterer. SATS Gateway Services provides airfreight and ground handling services including passenger services, ramp and baggage handling, aviation security services, aircraft cleaning and aviation laundry. SATS Food Solutions serves airlines and institutions, and operates central kitchens with large-scale food production and distribution capabilities for a wide range of cuisines.

SATS is present in the Asia-Pacific, the Americas, Europe, the Middle East and Africa, powering an interconnected world of trade, travel and taste. Following the acquisition of Worldwide Flight Services (WFS) in 2023, the combined SATS and WFS network operates over 225 stations in 27 countries. These cover trade routes responsible for more than 50% of global air cargo volume. SATS has been listed on the Singapore Exchange since May 2000. For more information, please visit www.sats.com.sg

Why Join Us

At SATS, people are our greatest asset and we build our success on the knowledge, expertise and performance of every contributor, by embracing diversity and uniqueness. As part of our holistic approach and commitment to embracing FAM (Fulfilling, Appreciated, Meaningful) in the workplace, we offer the runway to develop Fulfilling careers that foster your career growth, recognising and Appreciating the strength of talent and capabilities that we continue to build internally; and inspiring and encouraging each other to make Meaningful contributions in the work we do at SATS.

Key Responsibilities

The Role

The Lead, Data & AI Governance is responsible for driving the enterprise Data & AI Governance function across Data Governance, AI Governance, Master Data Management and Data Quality. The role defines the Group-wide strategy, framework and policy architecture; provides oversight, challenge and monitoring of adherence while business domains execute the controls. The role ensures data and AI assets are managed effectively, securely, ethically and in compliance with regulatory requirements.

Key Responsibilities

Strategy, Framework & Policy Architecture

• Define the Group-wide Data & AI Governance strategy, framework, operating model and multi-year roadmap across the four pillars, aligned to business strategy and AI ambitions.
• Author and maintain the Group's policies, standards, guidelines and procedures.
• Drive governance maturity through continuous improvement and performance metrics.

Governance Operating Model & Stewardship

• Design the governance model: define decision rights (RACI), and appoint, develop and sponsor domain Data Owners and Data Stewards across the business units.
• Translate governance topics into clear, business-relevant insights for the relevant audiences.

Data Governance, Catalogue & Metadata Standards

• Set the Group standards for data ownership, critical data elements, business glossary, metadata, data classification and dataset certification.
• Mandate data contracts on critical datasets and lineage coverage, embedding governance checks into the delivery lifecycle with platform engineering.
• Own the access governance standard and govern access to data, balancing innovation with appropriate controls.

Data Quality Management

• Own the Group data quality policy and standard - quality dimensions, rule libraries, thresholds and SLAs agreed with data owners.
• Oversee the data quality operating loop, per-domain scorecards, remediation to SLA and owner-accountability reporting.
• Set the dataset certification regime so critical datasets carry named owners, definitions, contracts and passing quality rules before use.

Data Protection, Sharing & Retention

• Work closely with Legal, Cybersecurity and Data Protection Office to develop and maintain a holistic data protection framework, controls and policies across the Group.
• Identify, assess and escalate data protection risks, agreeing risk acceptances at the appropriate level and tracking mitigations to closure.

Master Data Management

• Serve as design authority for master data capability, to centralize and manage master and reference data efficiently.
• Broker and secure ratification of master data design decisions - arbitrating cross-functional disputes on evidence.
• Drive change management efforts to ensure successful adoption and integration of the MDM platform across the organization.

Regulatory Compliance, Risk & Assurance

• Conduct regulatory horizon scanning across the jurisdictions where SATS operate, re-baselining policies, controls and plans as laws and standards evolve.
• Lead Group-wide Key Risk and Control Self-Assessment initiatives for Data and AI, and coordinate data and AI audits, compliance reviews and inspections with audit-ready evidence.

Leadership, Reporting & Culture

• Own the governance KPI metrics, dashboards, and reporting for executive stakeholders.
• Serve as the senior liaison between business units on governance requirements.
• Be a change agent inculcating data and AI governance culture and discipline across the organisation’s business and technical processes.

Key Requirements

• Bachelor's degree in Computer Science, Information Management, Data/Analytics, Engineering, Law, or a related discipline; a relevant postgraduate qualification is an advantage.
• At least 8 years of working experience in data management, including 5 or more years leading Data Governance, Data Quality or Master Data Management programmes in multi-entity or regulated organisations.
• Extensive knowledge of data and AI regulatory, standards, and compliance requirements.
• Experience in AI or model governance, model risk management or responsible-AI programmes, including generative AI; exposure to agentic AI governance is an advantage.
• Proven track record standing up or transforming a governance function and operating model.
• Good understanding of risk management and compliance frameworks and related disciplines such as risk and control self-assessment.
• Experience in designing and delivering a Master Data Management capability.
• Strong hands-on technical fluency: proficient in SQL with working Python; credible with modern data catalogues (e.g. DataHub, Microsoft Purview), data quality frameworks, data contracts and lineage, policy-as-code access models and Git / pull-request-based delivery.
• Relevant certifications such as Certified Data Management Professional (CDMP), Certified Information Management Professional (CIMP) or Certified Information Systems Auditor (CISA) are strongly preferred.
• Excellent written and oral communication; skilled at authoring policies, standards, guidelines and executive materials that business and engineering teams can act on.