I’m a QA Automation Engineer focused on building reliable, maintainable, and scalable test architectures.
My work goes beyond writing automated tests. I design automation systems that are readable, debuggable, and resilient in CI environments. Recently, I’ve been leading architectural refactors, migrating layered SitePrism-based page objects toward simpler, more explicit Capybara patterns to reduce cognitive overhead and improve traceability.
I’m particularly interested in test architecture, framework design, and the trade-offs between layered abstractions and integrated tooling. I enjoy questioning default patterns and evolving automation stacks toward clarity and long-term sustainability.
My core stack includes Ruby, Capybara, Selenium, and GitHub Actions, and I’ve been expanding into Playwright and modern TypeScript-based automation approaches.
Beyond automation, I care about quality as a product discipline — ensuring that tests validate meaningful user behaviour rather than just DOM structure.
- Automation & Testing: Capybara, Selenium, Playwright, RSpec, Cucumber
- Languages: Ruby, TypeScript, JavaScript
- CI/CD: GitHub Actions
- Architecture: Test framework design, abstraction strategy, refactoring legacy automation
- Quality Focus: Flakiness reduction, behavioural validation, maintainability
Advanced specialisation focused on modern end-to-end test automation using Playwright and AI-assisted workflows.
Key Areas:
- Scalable project architecture using Feature Actions
- Deterministic E2E strategies (network interception, mocking)
- API setup + UI validation hybrid testing
- Flaky test elimination strategies
- CI/CD pipelines with GitHub Actions
- Executive dashboards and TestOps practices
- AI-assisted test generation and code review
Focused on evolving from test script implementation to automation system design and operational quality engineering.
During my data science bootcamp, I worked on a variety of hands-on projects that allowed me to apply my knowledge and skills in real-world scenarios. I leveraged Python and popular data science libraries such as NumPy, Pandas, and Scikit-learn to perform data cleaning, exploratory data analysis, and predictive modelling.
- Contributor: Automation Patches / Extensions that allow you to extend your Ruby-based testing frameworks
I'm always open to interesting conversations and collaboration opportunities. Feel free to reach out to me via the following channels:
- LinkedIn: Marcelo Nicolosi
Let's connect and create amazing things together!