Senior Manager, Data & Artificial Intelligence Integration

Salary: $8,200 – $9,600 per month

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.

Applied AI, AI Concierge and Intelligent Automation

• Prioritise AI opportunities based on value, user need, data readiness, feasibility and risk, selecting from native applications, APIs, workflow automation, commercial AI services, custom development and RPA where appropriate.

• Lead the AI concierge and other applied-AI solutions from use-case definition and prototyping through evaluation, deployment, adoption and continuous improvement.

• Design or oversee retrieval-augmented generation (RAG), knowledge-assistant and appropriately bounded agentic-AI solutions using approved, version-controlled sources, identity-based permissions, citations, uncertainty and refusal behaviour, human escalation and auditable tool access.

• Define representative evaluation datasets and acceptance criteria covering correctness, groundedness, citation accuracy, privacy, safety, performance, cost and task completion.

• Define decision and action boundaries, human approvals and accountable owners for sensitive or high-impact use cases, ensuring AI does not make unauthorised employment, financial, care or client-related decisions.

• Monitor production AI for quality, unsupported answers, data leakage, security, performance, cost, adoption and unintended impact, supported by appropriate documentation, risk assessments, audit trails and incident-response procedures.

Analytics, Intelligence and Data Products

• Work with business and service teams to identify the decisions and client outcomes that analytics should support, and establish governed KPI definitions, calculation rules and owners.

• Develop reusable datasets, semantic models, dashboards and self-service analytics that are traceable to authoritative sources and supported by data-quality and reconciliation controls.

• Apply descriptive, diagnostic and predictive analytics where they provide clear value and can be used responsibly.

• Review data-product effectiveness, improving or retiring outputs that are no longer accurate, useful or actionable.

Product Delivery, Vendor Assurance and Capability Development

• Lead data and AI initiatives with business units, Enterprise Applications and OpsTech using appropriate delivery methods, with clear scope, ownership, priorities, milestones, acceptance criteria, risks, dependencies and decisions.

• Evaluate platforms, service providers and proposals, independently assessing architecture, data models, integration, security, testing evidence, costs, service levels and recovery arrangements.

• Ensure contracts and delivery arrangements provide MINDS access to the documentation, configurations, data, technical artefacts and knowledge required to operate, support, enhance or transition solutions.

• Manage user acceptance testing, production readiness, cutover, contingency planning, post-go-live stabilisation and support, and benefits realisation.

• Build and coach internal data and AI capability through engineering and review practices, knowledge transfer, data literacy, responsible-AI awareness and user adoption.

Qualification

• A good degree in Computer Science, Information Technology, Data Science, Data Engineering, Statistics, Engineering or a related discipline; equivalent qualifications and experience may be considered.

• At least eight years’ relevant experience across data engineering, data architecture, analytics, applied AI, enterprise integration or digital-product delivery, with substantial responsibility for enterprise data solutions.

• At least three years’ experience leading technical teams, multidisciplinary delivery teams or major data and AI workstreams.

• Proven experience leading or playing a substantial technical role in an enterprise data-platform or modernisation initiative, integrating operational systems and taking data, analytics or AI solutions into production.

• Experience managing technical vendors and independently assessing architecture, security, testing, costs and delivery evidence.

Other Information

• Modern cloud data and business-intelligence platforms such as Snowflake, Microsoft Fabric, Databricks, Power BI, Tableau or equivalent.

• Python or equivalent, Git-based workflows, command-line tools, modern development environments, and Jira and Confluence or equivalent delivery and knowledge-management tools.

• Model Context Protocol or equivalent agent-and-tool integration approaches, vector databases, and AI orchestration or evaluation frameworks.