Nicholas Borg, Data Engineer

§ 02

Selected Work

three projects

01

AI Job Matchmaker Agent

A multi-agent system that reads a CV, scrapes live job boards, and drafts a tailored cover letter for each shortlisted role.

Run on my own CV, it surfaced three strong matches (Data Analyst at Newton, Data Analyst at Foodsmart, and Lead Data Engineer at Open Architects), PDF in, letters out, in under a minute.

How I built it

Python Google ADK Gemini Multi-agent

Problem

Job hunting boils down to the same three tasks repeated: read a posting, decide if it fits, write a letter. I wanted to see how much of that a chain of agents could absorb.

Approach

I designed a pipeline of specialised agents that hand off to one another: parsing, retrieval, matching, and drafting. Built in Python on Google's Agent Development Kit, with the Gemini family handling the language work.

View notebook on Colab↗

02

Community Analysis of /r/Malta

A look at how /r/Malta actually talks: sentiment, what gets traction, what people argue about.

Three categories did most of the talking: local politics, tourism, and day-to-day grumbles. Sentiment skewed positive overall, more than I'd expected. Engagement clustered tightly around evening posting windows and a handful of recurring post formats.

How I built it

Python PRAW NLP Topic modelling

Problem

Reddit threads are loud and unstructured. I wanted to know what drives engagement on a country-specific subreddit, and whether sentiment moves in any predictable way.

Approach

Pulled historical posts via PRAW, then ran sentiment scoring and topic modelling on the corpus and visualised it. The same pipeline points at any community.

View notebook on Colab↗

03

Marvel Cinematic Universe: A Decade in Data

A breakdown of MCU box office, reviews, and casting patterns across 30+ films and ten years of releases.

Ensemble films beat solo outings. May releases over-perform. Critical scores barely correlate with box office. And audience taste has shifted between phases: Phase Four lands very differently from Phase One in both reviews and revenue.

How I built it

Python pandas Visualisation

Problem

The MCU is one of the most successful franchises in cinema, but the "why" is messy. Which signals (budget, timing, ensembles, scores) actually correlate with success?

Approach

Pulled data from box office records, review aggregators, and the MCU wiki, then ran correlations in pandas.

View notebook on Colab↗

§ 03

About

a brief introduction

Nick data engineer

My background is in cognitive science. The questions that pulled me in, how people think and make decisions, are still the ones I work with in code.

I use generative AI to dig up sources, test ideas, and tighten drafts. Data analysis and engineering give me another way to test the answers.

During the day, you'll probably find me working on a new data pipeline or playtesting the latest LLM. Outside of work, I'm always trying to perfect my Americano, dabbling in astrophotography, or running Mythic+ keys in Azeroth.

Background B.Sc. Cognitive Science

Based in Malta

Focus Data engineering · AI agents

Off-hours Coffee · Astrophotography · WoW

Get in touch Download CV

§ 04

Writing

essays & opinion

I've always enjoyed writing. It's how I figure out what I actually think. Articles tend to start as a tangle of half-formed ideas, then get slowly straightened into something readable.