Senior Manager, Data & Artificial Intelligence Integration

HYPERSCAL SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

MINDS in Singapore is seeking a Senior Manager, Data & AI to lead the enterprise data strategy and AI initiatives. You will translate priorities into a practical roadmap, build a governed data platform, and enable trusted data for operations, analytics and AI-enabled services.

Working with business units and external partners, you will ensure secure, reliable, interoperable solutions, oversee data models and pipelines, and drive responsible data practices, privacy and compliance with applicable

Responsibilities

• Supports the development of MINDS' enterprise data and AI strategy and leads its implementation.
• Translates strategic priorities into a practical roadmap and enables trusted data to support operations, enterprise applications, analytics and AI-enabled services.
• Leads data and AI products from discovery and solution design through implementation, evaluation, production deployment and adoption.
• Initial priorities may include the AI concierge and a shared enterprise data platform supporting staff productivity, organisational decision-making and better services for clients and caregivers.
• Working with business units, Enterprise Applications, OpsTech and external partners, ensures solutions are secure, reliable, interoperable and sustainable.
• Inspects data models, SQL, APIs, pipelines, configurations, logs and delivery evidence; guides internal teams; challenges vendors; ensures documentation, knowledge transfer, data portability, compliance and responsible-AI controls.

Job description

COMPANY DESCRIPTION

MINDS has been caring for Persons with Intellectual Disabilities (PWIDs) and their families since 1962.

Grounded in the belief that all PWIDs have innate abilities and talents, MINDS is committed to empowering them and enhancing their quality of life, while advocating for greater inclusivity for PWIDs as a nation.

MINDS is today one of the largest charities in Singapore employing more than 1200 staff. With the key focus to expand community-based services, improving the employability of PWIDs, strengthening education, and engendering inclusive community living, MINDS offers a holistic range of services and programmes across the PWIDs' lifespan.

These include schools, employment and training development centres, home-based care services and community-based services to cater to the physical, psychological, environmental and social needs of PWIDs. For more information, please visit www.minds.org.sg.

RESPONSIBILITIES

The Senior Manager, Data & AI supports the development of MINDS' enterprise data and AI strategy and leads its implementation. He/She translates strategic priorities into a practical roadmap, establishes a governed and scalable enterprise data platform, and enables trusted data to support operations, enterprise applications, analytics and AI-enabled services.

The role provides hands-on technical and delivery leadership across data architecture, engineering, integration, governance, analytics and applied AI. He/She leads data and AI products from discovery and solution design through implementation, evaluation, production deployment and adoption. Initial priorities may include the AI concierge and a shared enterprise data platform supporting staff productivity, organisational decision-making and better services for clients and caregivers.

Working with business units, Enterprise Applications, OpsTech and external partners, the Senior Manager ensures solutions are secure, reliable, interoperable and sustainable. He/She must have sufficient technical depth to inspect data models, SQL, APIs, pipelines, configurations, logs and delivery evidence; guide internal teams; challenge vendors independently; and ensure documentation, knowledge transfer, data portability, compliance with the Personal Data Protection Act and MINDS requirements, and responsible-AI controls.

Data Strategy, Architecture and Roadmap

Support the Head, Data & AI Development in maintaining MINDS' enterprise data and AI strategy, target operating model and implementation roadmap.

Assess the current data landscape and define the target architecture across source systems, integration, storage, master and reference data, metadata, analytics and AI.

Prioritise initiatives and develop phased business cases based on value, data readiness, feasibility, cost, risk and delivery capacity.

Define measurable outcomes for adoption, productivity, service quality, data trust, cost-effectiveness and operational impact.

Enterprise Data Platform and Engineering

Lead the design, implementation and continuous improvement of a governed enterprise data platform using an appropriate warehouse, lakehouse or equivalent architecture.

Establish reusable data-ingestion and integration patterns using supported APIs, webhooks, event-based integration, platform connectors and secure scheduled files.

Define data models, stable identifiers, transformation rules, orchestration, semantic structures and reusable data products.

Oversee the full data-pipeline lifecycle from development to production, including testing, deployment, scheduling, monitoring, exception handling, reconciliation and recovery.

Establish sound engineering practices covering development environments, Git-based version control, release management, documentation and production support.

Manage platform performance, availability, scalability, cost, backup and recovery, while maintaining the documentation, configurations and data-export arrangements required for knowledge retention, portability and responsible vendor or platform exit.

Data Governance, Quality and Security

Establish practical data-governance arrangements covering accountable data owners, data stewards, decision rights and escalation paths.

Define standards for data classification, business definitions, metadata, lineage, retention, archival and authorised use.

Implement data-quality controls covering completeness, accuracy, validity, consistency, uniqueness, timeliness and referential integrity, supported by recurring reconciliation across source systems and downstream products.

Apply identity-based access, least privilege, segregation of duties, encryption, audit logging and access reviews, with appropriate data minimisation, masking, anonymisation or pseudonymisation.

Ensure compliance with the Personal Data Protection Act, MINDS policies, information-security requirements and applicable incident-management procedures.