Senior Manager, Data & Artificial Intelligence Integration

MINDSG LTD; Otago; On-site; NZD 180,000 - 260,000; Full time

Job summary

MINDSG LTD seeks a Senior Manager, Data & AI to drive the enterprise data and AI strategy from roadmap to implementation, enabling trusted data for operations, analytics and AI services.

You will lead data architecture, engineering, governance, and applied AI across teams, partner with business units and vendors, ensure secure, scalable solutions, and deliver measurable outcomes in data trust and decision support.

Qualifications

• A good degree in Computer Science, Information Technology, Data Science, Data Engineering, Statistics, Engineering or a related discipline.
• Eight years of relevant experience across data engineering, data architecture, analytics, applied AI, enterprise integration or digital‑product delivery.

Responsibilities

• 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 target architecture across source systems, integration, storage, master/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.
• Lead design and implementation of a governed enterprise data platform using an appropriate warehouse, lakehouse or equivalent.
• Establish reusable data‑ingestion and integration patterns using APIs, webhooks, event‑based integration, platform connectors and secure scheduled files.

Skills

Data strategy

Data architecture

Data engineering

Applied AI

Leadership

Vendor management

Security/compliance

Education

Bachelor's degree or higher in Computer Science, IT, Data Science, Data Engineering, Statistics, Engineering or related discipline

Tools

Snowflake

Microsoft Fabric

Databricks

Power BI

Tableau

Git

Job description

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.