Jordan Regan

I started as a student passionate about sciences, driven by a curiosity for how the world works. During a challenging period in my life, I discovered the beauty of programming - not just as a skill, but as a turning point in my life that enabled newfound creativity and innovation. About six months later I was engineering large-scale batch data processes and administrating a Google Cloud Platform data warehouse for a company of thousands of employees - there was no going back now!

After a couple of years of data engineering, I was able to take the opportunity to learn enterprise backend development, which led me down new avenues in the world of data: real-time, event streaming with Kafka; federated GraphQL queries with Apollo; and learning all about project data requirements in everything from CRM integration to enterprise microservice architecture.

Four years into my data engineering journey and I am an accomplished cloud and on-prem pipeline aficionado with an ever-abounding thirst for knowledge and efficiency and a constantly developing background of maths and science on which to operate. Having found my footing in the more advanced areas of development such as: CI/CD; IaC; dependency management; parallel processing and the dreaded stakeholder management, to name a few, I now find myself in a position of confidence where the only blocker left standing in the way of my everyday work... is time.

As I prepare for a future defined by larger data volumes and AI powered processes, I have been skilling up in those data engineer techniques which serve as precursors to machine learning modelling. In the big-data front, I have gained hands-on experience with optimising high-throughput systems, as well as managing big-data clusters in AWS. In relation to machine learning directly, vector-indexing for semantic searches, data cleaning and preprocessing, and developing AI agents with integrated LLM wrappers and context management, are just some of the areas where I have been honing my skills to develop readiness for a world where data pipelines are inextricable from artificial intelligence.