Role Overview
The Director of Data Engineering is the senior technical leader responsible for building and running NHG’s data and AI engineering capabilities. Reporting to the Chief Data Officer, the role turns strategy into reliable platforms, pipelines, APIs and AI/ML infrastructure used by clinical, research and operational teams.
This is a hands-on leadership role in NHG’s data and AI transformation. The Director leads engineering delivery, architecture and standards, builds a multi-disciplinary team, and works with CDO, IT, analytics and clinical stakeholders to ensure NHG’s data infrastructure is secure, reliable, interoperable and scalable.
The role is accountable for technology delivery and for the clinical, operational and business outcomes enabled by data and AI engineering.
Scope and Interfaces
- Reports to the Chief Data Officer and leads the engineering function within the Data and AI Office.
- Owns engineering delivery, technical architecture, engineering standards, platform production readiness and engineering assurance for data and AI products, ensuring alignment with NHG Health’s technology partners, MOH requirements, and Singapore’s national data engineering strategy and digital health initiatives.
- Partners with the CDO to ensure the engineering roadmap is directly tied to measurable clinical, operational, research and population health outcomes, and that engineering investment decisions are communicated in terms of business value to senior stakeholders.
- Partners with IT, cybersecurity, enterprise architecture, infrastructure and procurement teams on hosting, cloud, network, identity, security, resilience and enterprise technology standards.
- Works with data governance, clinical informatics, responsible AI, privacy and compliance teams to translate policy requirements into technical controls.
- Collaborates with clinical, operational, research, population health and corporate stakeholders to convert use cases into scalable engineering solutions.
Key Responsibilities
1. Lead design of NHG data strategy and its translation into engineering delivery
Design and execute NHG’s data and AI strategy into a clear engineering roadmap, delivery plan and prioritised backlog aligned to clinical, operational, research and population health goals.
2. Build and run data and AI platforms
Design, build and operate secure, scalable platforms, pipelines, APIs and AI/ML infrastructure that support trusted data use across NHG with DataOps/MLOPs/AIOps capabilities.
3. Ensure reliability and production readiness
Set standards for performance, uptime, monitoring, incident response, documentation and continuous improvement of production data and AI service, with day 2 monitoring for AI capabilities.
4. Embed security, privacy and responsible AI
Translate governance, data protection, HIM and responsible AI requirements into practical engineering controls, audit evidence and safe operating practices.
5. Lead and grow the engineering team
Recruit, develop and lead a multi-disciplinary engineering team with strong technical discipline, delivery accountability, continuous learning and future skill proofing.
6. Manage technology and vendor choices
Evaluate tools, platforms, vendors and build-versus-buy options based on scalability, interoperability, security, cost, supportability and alignment with national technology initiatives in collaboration with health tech agencies.
7. Partner across NHG and national initiatives
Work with CDO, IT, cybersecurity, governance, analytics, clinical, corporate and national stakeholders to translate needs into practical engineering solutions and communicate progress, risks and value clearly.
Overview of Key Results Areas
The role will be assessed against key results areas that show how data and AI engineering enables NHG’s clinical, operational, research and population health priorities.
- Platform delivery: Secure, scalable and reusable data and AI platforms aligned with NHG and national priorities.
- Production readiness: Priority data products and AI solutions safely moved into monitored, sustainable use.
- Governance-by-design: Security, privacy, data governance, HIM and responsible AI built into engineering practices.
- Capability building: Strong engineering team with mature DevOps, DataOps, MLOps and production support practices.
- Value and impact: Reduced duplication, trusted data products, faster delivery and measurable clinical, operational and research value, and user satisfactions.
Key Requirements
Essential
- At least 12 years of experience in software engineering, data engineering, platform engineering or related technology leadership roles.
- At least 5 years leading multi-disciplinary engineering teams in complex enterprise environments.
- Strong technical depth in data engineering, cloud infrastructure, API design, integration architecture, modern software engineering and production operations.
- Proven experience delivering enterprise-grade platforms or digital systems with strong reliability, security, supportability and governance expectations.
- Experience with DevOps, DataOps, ALOps and or MLOps practices, including CI/CD, automated testing, monitoring, observability and infrastructure-as-code.
- Demonstrated ability to communicate engineering strategy, technical trade-offs and delivery risks in business and clinical terms to senior non-technical stakeholders.
- Experience managing engineering cost transparency, total cost of ownership and value-for-money reporting.
- Ability to communicate technical trade-offs, delivery risks, value and implementation options clearly to clinical, operational, governance and executive stakeholders.
Preferred
- Healthcare, public-sector or other regulated-industry technology experience.
- Experience with AI/ML platforms, MLOps, LLM-enabled solutions, generative AI or agentic AI in production settings.
- Familiarity with healthcare interoperability, clinical safety, data governance, data protection and HIM-related requirements.
- Experience managing vendors, technology partners, build-vs-buy decisions and value-for-money assessments in enterprise settings.
- Familiarity with Singapore's national technology landscape, including GovTech platforms, MOH digital health initiatives or whole-of-government cloud and data infrastructure.
- Bachelor's degree in Computer Science, Engineering or a related field; a Master's degree is an advantage.
General JD Disclaimer
This job description is intended to indicate the general nature and level of work expected of the role. It is not an exhaustive list of duties, responsibilities or qualifications, and may be refined as NHG's data and AI operating model matures.