Lead, Data & AI Governance

About SATS

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, 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 215 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.

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Job Description

The Lead, Data & AI Governance responsible for driving the enterprise Data & AI Governance function across four pillars - 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 Lead partners closely with business, technology, risk, compliance, legal, privacy and cybersecurity stakeholders across SATS' global network to ensure data and AI assets are managed effectively, securely, ethically and in compliance with regulatory requirements.

The successful candidate will translate complex data and AI governance concepts into clear, business-relevant insights, embed governance into engineering delivery, and build a culture of data accountability and responsible AI.

Location: Mumbai/Singapore

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 policy architecture - policies, standards, guidelines and procedures (e.g. data governance and ownership, data classification and handling, AI acceptable use and risk, data quality, master data, records and retention).

· Provide oversight, challenge and monitoring of adherence to policies and standards.

· Lead the continuous-improve

-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, building confidence across teams.

-Data Governance, Catalogue & Metadata Standards

· Set the Group standards for data ownership, critical data elements, business glossary, metadata, data classification and dataset certification, and oversee their implementation in the enterprise data catalogue.

· 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 Cybersecurity and Data Protection Office to develop and maintain a holistic data protection framework and policies across the Group.

· Oversee data-sharing arrangements across business units, subsidiaries and external parties, and adherence to enterprise retention and disposal standards.

· Collaborate with Legal, Privacy and Compliance to ensure appropriate controls for sensitive and regulated data across jurisdictions.

· 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 - system of entry versus system of record, consumers and contracts - arbitrating cross-functional disputes on evidence.

· Drive necessary change management efforts to ensure successful adoption and integration of the MDM platform across the organization, and set the scaling path to further domains.

-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 and technology so governance requirements are understood, prioritised and planned.

· Be an agent of change inculcating data and AI governance culture and discipline across the organisation’s business and technical processes.

Educational Qualifications

· Bachelor's degree in Computer Science, Information Management, Data/Analytics, Engineering, Law, or a related discipline; a relevant postgraduate qualification is an advantage.

· Ongoing commitment to training and professional development.

Relevant Skills and Experience

-Leadership & Experience

· 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.

Personal Characteristics & Behaviours

· Strong ownership and accountability mindset.

· Pragmatic approach.

· Clear communicator across technical and non-technical stakeholders at all levels.

· Team player with strong cross-functional collaboration skills.

· Detail-oriented.

· Ability to operate effectively in a complex, fast-moving, globally distributed environment.

Travel Requirements

Occasional travel to SATS locations may be required.