Greater Melbourne Area
Data engineer and scientist with a PhD in Materials Physics and a track record of solving complex data challenges for robust, production-grade platforms. I bring the analytical rigour of a research background to every stage of the engineering lifecycle - from scoping requirements with stakeholders to delivering scalable pipelines and actionable outcomes. Over the past five years, I've worked across energy, financial services, education and startups, leading teams and delivering platforms on Databricks and AWS. Recent work spans emissions reporting for Net-Zero Banking Alliance compliance, reporting on fault identification and DER services for electricity metering providers, and AI/data maturity assessments for continuing education institutions.
Client Engagements: - Data Technical Lead - Climate Data & Insights, Financial Services Client, 07/2024- 05/2025
Client Engagements: - Data Technical Lead, Financial Services Client, 09/2023 - 07/2024 - Senior Data Engineer, Financial Services Client, 04/2023 - 09/2023
As a data engineer in the Data & AI Service, I work across a range of fields and technologies including: - Generative AI - Sustainable technology - Privacy enhancing technologies - Large language models - Data mesh - TinyML
As lead data engineer, I focused on developing processes for R3's analytics and data science workloads. As part of these efforts, I started a small, on-prem k8s cluster for machine learning experiment tracking (MLOps) and also established analysis and reporting workflows on AWS using serverless offerings to track data usage of deployed products. I also had the opportunity in this role to develop an open-source C# .NET library for monitoring microservices with Vigil.
In this role, I worked across the entire tech stack of the business, working with software engineers to devise solutions for data analysis pipelines, API & Data Warehouse design, embedded linux firmware, encryption & security.
Collaboration with the Ditchley Foundation as part of part of S2DS project-based data science course hosted by Pivigo. We delivered a toolkit for the Ditchley Foundation which uses graph database technology and NLP topic modelling to discover domain experts who are influential on social media. The final product had a success rate >90% for identifying relevant twitter accounts.