I began my career in accounting and finance, working as a Chartered Accountant, before moving into data science to deepen my analytical and technical skill set. That led me to a Master of Applied Data Science, and from there into geospatial development, building data engineering solutions and interactive tools for infrastructure planning and research. Along the way, that work pulled me further into software development, and I'm now studying part time towards a Master of Applied Computing to build on that foundation.
What's stayed consistent throughout is an interest in raw, complex, and messy data, whether it's financial records, environmental data, or spatial datasets, and turning it into something reliable and useful.
I enjoy cleaning and structuring data, building and automating data pipelines, working across databases, and developing models, dashboards, and reports that hold up under scrutiny. The goal is always the same: make the data work smarter, so people relying on it can make better decisions with less effort.
Developed geospatial data engineering solutions and contributed to building interactive web-based applications. Owned the full data lifecycle for the Digital Twin platform, from data acquisition and ingestion through storage, processing and transformation, validation, and analysis and visualisation, and conducted robust unit testing of the codebase. Automated and maintained data pipelines that retrieved, integrated, and validated raw spatial and environmental data from multiple external agencies in varying formats, ensuring data quality and reliability, then processed it into standardised model inputs and fed model outputs back into the Digital Twin to support infrastructure planning and evidence-based decision-making.
Tutored and supported students in lab sessions across data science coursework.
Conducted data analysis on Airbnb listings across New Zealand, created visualisations and maps to derive insights, identify trends, and track changes over time during the COVID-19 pandemic. Applied machine learning clustering techniques to group listings into distinct types based on their characteristics. Built an interactive R Shiny dashboard to communicate analytical findings, provide data-driven insights, and support user exploration of spatial and temporal trends.
Developed and maintained interactive dashboards and weekly performance reports covering sales, manufacturing, wastage, and payroll to provide key business insights. Prepared monthly reports highlighting key performance trends, metrics, and variances against budget and prior periods to give senior leadership clear insights for decision-making. Prepared monthly financial statements, balance sheet reconciliations, GST returns, and management reports. Maintained the fixed asset register and assisted with budget preparation. Supported the wider finance team by providing backup for Payroll, Accounts Receivable, and Accounts Payable.
More projects to be added soon.
A Digital Twin prototype for efficient flood risk management that integrates spatial data from multiple agencies, automates BG-Flood scenario modelling, and delivers the results through an open-source web interface.
Read post ↗A closer look at the Digital Twin data pipeline, covering automated LiDAR-based terrain processing, rainfall hyetograph generation, tide and sea-level rise data, and river inflow hydrographs, all standardised as inputs for BG-Flood.
Read post ↗The NewZeaLiDAR project is an open source framework that automatically discovers, organises, and processes LiDAR datasets across New Zealand to generate hydrologically conditioned digital elevation models for any requested area of interest.
Read post ↗A platform for collecting and communicating knowledge about the Ōtākaro/Avon River, guided by the Te Mana o te Wai framework to support environmental decision-making, with a VR visualisation of a potential future restoration scenario.
Read post ↗How well can you stack?
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