Omer Tarik Yaylagul

Senior QA Automation Engineer, SDET, and Systems Engineer with 10 years of experience building, modernizing, and scaling automation across The Walt Disney, T. Rowe Price, Pizza Hut/Yum! Brands, government platforms, fintech, logistics, and enterprise technology.

Grounded in Aerospace Engineering, Air Force military aviation education, and flight training, bringing disciplined execution, systems-level thinking, situational awareness, and a methodical ability to break complex systems into solvable components, isolate root causes, and engineer practical solutions. Known for stepping into complex or unfamiliar environments, rapidly understanding how systems work, identifying root causes others have struggled to isolate, and engineering solutions that teams can continue using long after the immediate problem is solved. Rapidly learns new technologies and becomes a trusted, go-to engineer for high-impact problems requiring technical depth, persistence, adaptability, and ownership.

Sr. QA Automation Engineer / Sr.

Systems

Engineer - THE WALT DISNEY COMPANY - Anaheim, California

(2024-09)

• Designed and built a Python automation framework from scratch using PyTest and Robot Framework for Jira Data Center and Confluence Data Center, establishing scalable functional, regression, integration, plug-in, permission, and enterprise-platform test coverage across Disney Experiences.
• Owned automation quality across Development, Test, and Production environments for an Atlassian ecosystem supporting approximately 1,500 projects, where platform defects could affect large numbers of downstream teams and business workflows.
• Validated approximately 30 Atlassian Marketplace apps, plug-ins, and Confluence macros across ~1,500 projects, testing hundreds of configuration and usage combinations across Jira issues, issue types, workflows, boards, dashboards, JQL, filters, custom charts, project settings, custom fields, screens, permissions, spaces, pages, macros, links, and admin configurations to identify compatibility, permission, integration, and functional defects before production deployment.
• Engineered automated Jira Data Center cluster-node verification that eliminated repeated manual login/logout checks across multiple nodes, and created an HTTP-header injection solution that restored automation execution during authorized maintenance-mode windows.
• Served as Tier 2/3 escalation owner for Jira and Confluence production issues, resolving live user tickets and coordinating complex troubleshooting with Atlassian, Xray, Marketplace vendors, and internal Disney engineering teams.
• Took key QA ownership for migrating approximately 1,500 Jira and Confluence projects from Data Center to Atlassian Cloud, re-engineering Robot Framework tests and validating issues, boards, dashboards, charts, workflows, apps, macros, users, groups, teams, permissions, security, spaces, pages, and access controls before and after migration.
• Created migration test strategies, validation checklists, regression coverage, execution procedures, technical documentation, and operational runbooks; communicated readiness, risks, defects, and platform quality to senior managers, directors, engineering leaders, and project stakeholders.
• Governed and validated enterprise Jira/Confluence configurations including permission schemes, workflows, screen schemes, issue types, custom fields, groups, global permissions, project security, and administrative controls while extending into System Engineer / Platform Engineer responsibilities.
• Used advanced JQL, SQL-oriented logic, Groovy, and ScriptRunner to automate platform-level operations and reporting; used Jira, Confluence, and Xray REST APIs plus Postman/GraphQL to retrieve, validate, analyze, and modify data programmatically.
• Integrated and validated Tricentis qTest, Jira Xray, Smartsheet, and third-party systems, including bidirectional Jira-Smartsheet synchronization and project-level qTest configurations across multiple Jira projects.
• Applied Atlassian Rovo, Claude, Claude Cowork, and ChatGPT daily for debugging, automation development, API testing, code review, ticket analysis, planning, and complex platform troubleshooting, accelerating execution across concurrent engineering responsibilities.
• Used Linux and macOS (MacBook) Terminal and command-line tools for daily troubleshooting, including navigating file systems, inspecting logs, managing processes, running Python/PyTest/Robot Framework automation, validating environment configuration, and diagnosing application, API, and platform issues across local and enterprise test environments.
• Mentored and guided other QA engineers on what to test and how to test it across Jira and Confluence changes, helping define risk-based coverage for workflows, permissions, integrations, plug-ins, migrations, regression scope, test data, and production-impact scenarios; reviewed test approaches and shared reusable validation practices.
• Helped design and deliver an AI support agent integrated with Microsoft Teams and Jira Cloud, enabling users across approximately 1,500 Jira projects to receive real-time Jira assistance within controlled permissions and automatically escalate unresolved conversations into structured Jira tickets containing problem summaries, troubleshooting context, screenshots, and supporting media for System/Platform Engineers.

Senior QA Automation Engineer - T. ROWE PRICE - Baltimore, Maryland

(2023-09 - 2024-09)

• Delivered automation quality within T. Rowe Price's global investment-management environment, a firm managing approximately $1.45T in assets at year-end 2023, where reliable software supports large-scale financial, retirement, and investment services.
• Brought in to recover and stabilize a large, business-critical legacy Cypress automation framework containing extensive broken and unreliable UI test coverage; reverse-engineered a Cypress/TypeScript codebase developed by multiple engineers, tracing interconnected UI flows, dependencies, selectors, reusable components, and business logic to restore failing automation without destabilizing downstream coverage.
• Restored previously unusable automation by tracing failures across interconnected workflows, identifying root causes, repairing damaged logic, and implementing sustainable fixes rather than temporary workarounds.
• Improved automation test pass rates by approximately 35% through systematic debugging, selector stabilization, synchronization improvements, framework maintenance, and targeted refactoring of unreliable Cypress tests.
• Identified inefficient Cypress wait and synchronization strategies that significantly extended regression execution times; optimized test logic to eliminate unnecessary delays and deliver faster, more reliable release feedback.
• Re-engineered legacy JavaScript/TypeScript components in