Director of Data

JobsCloseBy Editorial Insights

Onit is seeking a Director of Data in Auckland to lead a data organization and drive scalable, trusted data capabilities across the business. You’ll manage data scientists, AI engineers, and MLOps engineers to power products, own the data strategy and platform vision, and ensure governance, privacy, and compliance while enabling AI R&D and decision intelligence. Priorities include building end-to-end data pipelines, real-time capabilities, data mesh thinking, and DataOps alignment with Product and Engineering. To stand out, highlight 8+ years in data engineering or analytics platforms, 3+ years leading senior teams, AWS and containerization familiarity, and quantified value from data initiatives; provide concrete examples of scalable foundations, datasets, and compliant pipelines.

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We are seeking a Director of Data to lead our data function and drive the development of scalable, trusted, and business-critical data capabilities across the organisation. You will manage a high-impact team of data scientists, AI Engineers, and MLOps Engineers to ensure our enterprise products are powered by high-quality, well-governed, and accessible data.This is a senior leadership role reporting to the SVP of AI Labs, operating at the intersection of data architecture, governance, platform scalability, and measurable business outcomes.You will play a foundational role in enabling core AI R&D, analytics, and decision intelligence through strong data strategy and execution.

Key Responsibilities

• Team Leadership & Technical Management
•  Lead and manage a team of data scientists, AI Engineers, and MLOps Engineers.
• Provide technical mentorship and foster a culture of engineering excellence, ownership, and continuous improvement.
• Set clear goals, performance metrics, and growth plans for team members.
• Recruit and retain world-class data talent across engineering, analytics, and governance disciplines.
 
• Data Strategy & Platform Vision
• Define and own the organisation’s data strategy, including warehouse architecture, ingestion pipelines, and analytics foundations.
• Identify emerging best practices in modern data infrastructure (e.g., real-time streaming, ELT frameworks, data mesh concepts) and assess applicability.
• Champion scalable and reusable data foundations that support both AI innovation and enterprise reporting needs.


• Enterprise Data Engineering & Delivery
• Oversee the design and implementation of robust data pipelines for structured and unstructured enterprise data.
• Ensure data systems support product innovation, operational workflows, and AI-native architectures.
• Partner with Product and Engineering leadership to prioritise data initiatives that deliver measurable business value.


• Data Pipelines for AI and Model Enablement
•  Lead the development and scaling of end-to-end data pipelines that support machine learning and large language model workflows, including training, fine-tuning, evaluation, and inference.
• Ensure high-quality, well-governed datasets are available for model development, including structured business data and unstructured legal documents.
• Partner closely with AI Research and Engineering teams to define data requirements for experimentation, benchmarking, and production deployment.
• Establish repeatable processes for dataset versioning, feature generation, and continuous refresh to support ongoing model improvement.
• Implement strong controls for privacy, anonymisation, and compliance when using enterprise or client-derived data in AI pipelines.
 
• Data Governance, Trust, and Compliance
• Establish strong governance frameworks for data quality, lineage, access controls, and compliance.
• Ensure responsible handling of sensitive enterprise and legal-domain data, aligned with AI ethics and regulatory requirements.
• Define standards for metadata management, documentation, and auditability across all data assets.
 
• Data Enablement for AI and Analytics
• Collaborate closely with AI Engineering and Research teams to ensure data readiness for model training, evaluation, and deployment.
• Enable self-service analytics and trusted reporting across business teams.
• Bridge data engineering with downstream consumption, including dashboards, AI features, and embedded intelligence.
 
• Data Systems Development & Deployment
• Oversee deployment of data platforms and services using best-in-class DataOps and DevOps practices.
• Partner with infrastructure teams to ensure scalability, performance, reliability, and cost efficiency of data systems.
• Contribute to architecture decisions across APIs, backend services, and enterprise integration workflows in the data platform.

Qualifications

• Required
•  Master’s degree or equivalent experience in Computer Science, Data Engineering, Information Systems, or related field.
• 8+ years of experience in data engineering, analytics platforms, or large-scale data systems.
• 3+ years managing senior technical teams (Data Engineers, Platform Leads, or Analytics Engineering teams).
• Demonstrated ability to build and scale enterprise-grade data infrastructure from strategy through execution.
• Strong experience with modern data stack concepts, API integration, and production backend environments.
 
• Preferred
• Experience in enterprise software, legal tech, or other regulated industries.
• Familiarity with cloud services (AWS preferred), containerisation, and distributed data processing frameworks.
• Exposure to AI/ML enablement, feature pipelines, and governance for model training datasets.

Success Metrics

• Scalable and reliable data platform delivery across multiple product lines.
• Improved data quality, governance maturity, and organisational trust in enterprise data.
• Strong enablement of AI initiatives through compliant, model-ready data pipelines.
• Adoption of data-driven decision-making across engineering, product, and business teams.
• Recruitment and development of a high-performing data engineering organisation.