Senior QA Engineer · Automation Specialist

Quality that
ships with confidence.

I design and build automation ecosystems that give teams the confidence to release every sprint — from functional and visual testing to performance and security pipelines.

Based inBrazil
4+ years in QA
Web · Mobile · CI/CD
$ quality-pipeline --runbuild: passing · every sprint
  1. functional
  2. visual
  3. accessibility
  4. performance
  5. security

Case Studies

CASE 01

AI-Driven Mobile Testing Framework

MCP-based integration between Jira, TestRail, and Maestro to generate, orchestrate, and evolve test strategies automatically.

  • AI
  • Mobile
  • CI/CD
Automated
Test case generation from Jira tickets
3 systems
Jira, TestRail, and Maestro unified via MCP
Expanded
Mobile test coverage within a tight timeline
Tools used:
  • MCP
  • Jira
  • TestRail
  • Maestro

Context

The project operated under a closed scope and a tight delivery timeline, which made manually writing and maintaining test cases a real constraint. Coverage needed to grow quickly without the team having the capacity to build it from scratch. The question was how to scale mobile test coverage within the time and resource boundaries that existed.

What was built

The team built a framework using MCP (Model Context Protocol) as an integration layer between three systems: Jira for requirements, TestRail for test management, and Maestro for mobile UI automation. The framework reads tickets and acceptance criteria from Jira, generates and syncs test cases into TestRail, and produces or updates Maestro test flows — creating a pipeline where test strategies evolve automatically as product requirements change.

I contributed to defining the framework architecture and focused on expanding test coverage as the suite was built out, working alongside the team to validate and extend the generated flows.

Key aspects

MCP as the integration layer

Rather than building brittle point-to-point integrations between tools, MCP provided a structured protocol for connecting multiple systems with consistent context sharing — making the AI layer aware of requirements, test state, and automation results simultaneously.

Maestro as the automation target

Maestro's YAML-based, human-readable syntax made it a natural fit for AI-generated test flows — the output is reviewable and editable by engineers, not a black box.

Engineers in the loop

Generated tests were proposed, not auto-merged. Engineers reviewed and approved flows before they entered the suite, preserving quality control while removing the blank-page burden of writing coverage from scratch.

Outcomes

$ qa-report --case case-01

  • Automated — Test case generation from ticket requirements
  • 3 systems — Unified into a single intelligent pipeline
  • Faster — Coverage expanded within a constrained timeline
  • Scalable — Test strategies evolve as requirements change

CASE 02

Multi-Brand Digital QA Ecosystem

End-to-end QA strategy for a major restaurant group’s web, mobile, and loyalty platforms across multiple brands.

  • Web
  • Mobile
  • CI/CD
Every sprint
Production release cadence achieved
< 1 day
Full regression cycle time
5 layers
Functional, Visual, Accessibility, Performance and Security
Tools used:
  • Cypress
  • Percy
  • Axe-core
  • k6
  • OWASP ZAP
  • Maestro
  • Robot Framework

Context

Early in my career, I joined a cross-functional team responsible for expanding the digital presence of a major restaurant group. The scope was significant: improve partially built native mobile apps, build a new website from scratch — including a CMS (Sitecore) and an integrated ordering experience — and eventually absorb additional brands under the same umbrella, including their loyalty platform.

The project demanded a QA strategy that could scale across multiple brands, multiple platforms, and a growing team.

What was built

The automation coverage spanned five dimensions, all running automatically on every push to the QA environment:

Functional testing via a Cypress-based web suite with Page Object architecture built for scale and multi-brand reuse. Visual regression via Percy to catch layout and component breakage on a CMS-heavy frontend. Accessibility checks via Axe-core covering WCAG compliance across page templates. Performance load testing via k6 targeting key user flows in staging. Security scanning via OWASP ZAP integrated directly into the CI/CD pipeline.

Key decisions

Cypress over Java + Selenium

I led two structured POCs and ruled out Java + Selenium despite it being the familiar choice. The onboarding complexity didn’t fit a team that included interns, and the verbosity would slow delivery. With multi-brand expansion already anticipated, I chose Cypress and designed the Page Object architecture with that scale explicitly in mind.

Percy over BackstopJS

I identified that functional assertions alone wouldn’t catch visual regressions on a CMS-driven, image-heavy frontend. After evaluating both tools, Percy won on CI/CD integration quality and baseline management reliability.

k6 over JMeter

Performance testing wasn’t on the original roadmap. I identified the gap, made the case for its inclusion, and chose k6 over JMeter for its code-first approach and native fit with our pipeline — JMeter’s GUI-oriented model would have created unnecessary friction.

Deferring Robot Framework rewrite — then migrating at the right time

I spotted structural problems in the existing mobile suite early but assessed that an immediate rewrite would be more disruptive than valuable. I made the deliberate call to hold, and later led the architectural migration to Maestro when the project maturity and team capacity justified it.

OWASP ZAP as a pipeline gate

I integrated ZAP directly into CI/CD so security scans ran automatically on every build, treating security as a continuous delivery concern rather than a last-minute check.

Outcomes

$ qa-report --case case-02

  • Every sprint — Production releases — no regression backlogs blocking delivery
  • < 1 day — Full regression cycle, replacing what used to take several days manually
  • Multi-brand — Architecture scaled to additional brands with minimal rework
  • 5 layers — Functional, visual, accessibility, performance and security — all automated

Other Projects

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