Modern QA and Data Practice

Muhammad Ali Akbar is a quality assurance automation engineer with a cross-functional delivery and analytics background.

I currently work in QA automation at Governata and Braincell, after earlier QA work in e-commerce, data analysis, materials engineering, and project supervision.

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QA automation work across web, mobile, API, and release-focused testing.
Data analysis experience that strengthens reporting, pattern recognition, and decision support.
Engineering and supervision experience that supports structured execution in complex environments.

Overview

Quality automation with an analyst's lens

My path into QA automation has included product-facing QA work at Governata and Braincell, earlier e-commerce QA at Kataria, data analysis work at KeyTiger and freelance, and before that materials engineering and project supervision. That progression shaped a practical way of working: understand the system, reduce ambiguity, and make outcomes easier to trust.

Translate business and product requirements into testable, measurable work.
Use automation and data to uncover risk, patterns, and delivery gaps.
Support delivery with practical communication, structure, and follow-through.

Release confidence

Balancing coverage, speed, and maintainability across testing and validation workflows.

Decision support

Turning data into clear narratives for trends, priorities, and operational visibility.

Product empathy

Reducing ambiguity and helping teams make decisions with more confidence.

Highlights

Experience snapshot

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Academic degrees

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Professional certifications

Capability Stack

Tools I use across QA automation, API testing, performance work, and reporting

Selenium

Browser automation for regression coverage and critical user journeys.

JMeter

Performance testing, API load checks, and heavier traffic simulations.

Postman

API validation, collections, environment management, and integration checks.

Appium

Mobile automation for Android workflows and end-to-end app validation.

Jira

Defect tracking, test cycle coordination, and release visibility across teams.

Python

Automation scripting, data handling, and practical QA support utilities.

MySQL

Querying, data checks, and relational validation for backend-heavy systems.

PostgreSQL

Structured data work for reporting, investigation, and product analysis.

Power BI

Stakeholder dashboards and clearer reporting around quality and trends.

Tableau

Visual exploration and presentation of findings for non-technical audiences.

Pandas

Cleaning, reshaping, and understanding test or business datasets.

Scikit-learn

Model comparison, experimentation, and applied machine learning work.