Member Spotlight: Mayowa Fajobi — From Linux Roots to Platform and Open Source Work

# Who Mayowa Fajobi Is Mayowa Fajobi is a contributor to DZone who works across platform engineering, AI-driven solutions, and open source strategy. He's described his tech journey as growing out of a fascination with using technology to turn complex problems into repeatable, automated processes.

# How his career started Mayowa's interest solidified when he began working with Linux and infrastructure. Automating manual tasks showed him the value of repeatability and systems thinking. That experience moved him into DevOps, cloud-native stacks, and Kubernetes, and then into open source as a way to contribute to the tools he used.

# The three tools he can't live without Mayowa lists Linux, Git, and Kubernetes as his three essential tools. He explains why each matters:

• Linux gives a foundational understanding of what happens beneath applications and platforms.
• Git is central to collaboration and open-source participation.

He also mentions the terminal as an almost-essential fourth tool.

# Practical advice for newcomers Mayowa's advice is concrete and action-oriented:

• Contribute to open source early. Small contributions—documentation edits, tests, minor bug fixes—teach how real-world software is designed and maintained.
• Focus on solving real problems rather than amassing technologies.

# His view on where engineering is headed

What platform engineering should do now:

• Treat infrastructure as a product. Build strong platform foundations that users can rely on.
• Design for automation, portability, and policy-driven governance rather than rigid, fixed setups.
• Create safe experimental pathways for engineers to try AI while maintaining security and visibility.

# Outside work Mayowa balances technology with family time, travel, and music. He plays piano and tries to switch off, though he admits an open-source problem often pulls him back to his laptop.

# Where to find his work

# Bottom line Mayowa's path illustrates a practical route into modern platform work: start with systems fundamentals, use hands-on practice and open source to level up, and design platforms for automation and dynamic workloads as AI changes runtime behavior.