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A Python-powered analytics dashboard for the Australian job market — tracking remuneration trends, role distribution, and hiring data across data analyst positions. Built to practice and demonstrate real-world data engineering skills.
DAJobs (Data Analyst Jobs) is an analytics platform that scrapes, processes, and visualises job listing data for data analyst roles across Australia. The goal was simple: turn the noise of job boards into actionable insights — what skills are in demand, what salaries look like, where the roles are, and how the market is shifting.
It runs on a secure subdomain at dajobs.wfdnelson.com and uses Streamlit as the frontend framework — chosen for its fast iteration cycle and data-friendly component library.
Salary ranges scraped and normalised from job listings — see what data analyst roles actually pay across different seniority levels.
Interactive charts showing job volume by category, location, and required skills. Spot what employers actually want.
Where are the roles? City-level breakdown of job concentration across Australian markets.
Which tools and tech stacks keep showing up in job descriptions? Ranked by frequency across listings.
Time-series data showing how the market has moved — useful for timing applications and understanding demand cycles.
Deployed on its own subdomain behind nginx, with TLS — proper production setup, not just a dev server.
I was looking at the data analytics job market to understand what skills to prioritise — and found that job boards are noisy and inconsistent. Rather than manually scanning listings, I built a tool to do it for me.
DAJobs is a personal utility that became a portfolio piece. It demonstrates end-to-end data work: scraping, cleaning, analysis, visualisation, and deployment. The kind of pipeline a data analyst is expected to build.
Live at dajobs.wfdnelson.com