The Problem: The New Zealand water sector was hampered by inconsistent data practices across different councils. This created significant friction for data sharing and increased costs ahead of major national reforms that would require data amalgamation.

My Approach: I led a cross-sector working group to define the Three Waters Asset Data Standard (3WADS), establishing a common language for asset data. To make this standard easy to adopt, I developed and open-sourced a Python CLI toolkit that automates data validation, mapping, and the generation of schema connectors (XSD, SQL).

The Outcome: The standard and toolkit have provided a clear, low-cost pathway for data standardisation. This work is improving collaboration, reducing integration costs, and building a more resilient data ecosystem for the entire sector.

The Problem: A critical legacy asset management system had a fragile, 24+ hour data export process. This delay made timely reporting impossible and blocked the use of its 2 million+ asset records for modern analytics, while other business processes still depended on the old data format.

My Approach: I architected and built a new end-to-end ETL pipeline from the ground up. Using Ruby for extraction and Python (with Polars and Joblib for high-speed parallel processing), the pipeline transforms the data and produces two distinct outputs: modern analytics-ready Parquet files for our AWS data warehouse, and a backwards-compatible ESRI File Geodatabase to ensure zero disruption to existing systems.

The Outcome: The new pipeline reduced data processing time by over 95% (to just 30 minutes). This unlocked near-real-time operational analytics for the first time and provided a stable, modern data platform for the future, all while ensuring complete business continuity.

The Problem: A NZ$1.4B national transport project needed a robust framework to manage the quality and consistency of digital information from dozens of suppliers. Without it, the project risked receiving poor quality data that would create significant issues during operations and maintenance.

My Approach: As a key member of the Digital Engineering team, I co-authored the project's strategic information management framework based on ISO 19650. I was responsible for embedding these requirements into contracts, governing the Common Data Environment (CDE), and developing scripts to audit supplier data submissions for compliance.

The Outcome: The framework established a clear "single source of truth" for project information, ensuring data integrity from the supply chain. This proactive assurance work significantly reduced data integration risks and will maximize the long-term value of the project's digital assets.

The Problem: Capital projects were often delivered without clear requirements for the final asset data handover. This resulted in inconsistent, poor-quality data that required costly remediation before it could be used by operational and asset management teams.

My Approach: I authored and published the organisation's official Asset Information Requirements (AIR) in accordance with the ISO 19650 standard. This document clearly defines the data formats, structures, and quality standards for all new assets delivered through the capital programme.

The Outcome: The published AIR is now a contractual requirement for all new projects. It ensures high-quality, structured data is captured from project inception, significantly reducing future data cleaning efforts and providing reliable data for better decision-making across the asset lifecycle.