Alex Morgan

Lead quality assurance initiatives for cloud-native SaaS platform serving 2M+ users. Own test automation strategy, mentor junior engineers, and drive AI-assisted tooling adoption to accelerate delivery without compromising quality.

End-to-End Automation Platform Rewrite

• Migrated legacy Selenium test suite to Playwright with TypeScript, implementing parallel execution and flaky-test detection. Built custom test data factory using Python and Faker for deterministic, isolated test scenarios.
• Playwright, TypeScript, Python, Docker, Kubernetes, GitHub Actions, PostgreSQL
• Designed architecture, implemented core framework, trained team of 4 engineers, established CI integration and reporting dashboards.
• Tech Stack: Playwright TypeScript Python Docker Kubernetes CI/CD Pipeline Design
• Challenges: 70% Faster Test Execution: Reduced full regression suite from 4 hours to 72 minutes through parallelization and selective test targeting.
• Challenges: Flaky Test Rate Under 1%: Implemented automatic retry with root-cause logging and test stability scoring, dropping flaky tests from 12% to 0.8%.

AI-Assisted Code Review and Test Generation

• Piloted Claude Code and GitHub Copilot for supervised test generation and PR review workflows. Established guardrails requiring local validation, CI checks, and human review before any AI-suggested changes merged.
• Claude Code, GitHub Copilot, GitHub Actions, pytest, Python, REST APIs
• Evaluated AI tools for test generation accuracy, created task decomposition templates for repository-context exploration, implemented model-routing fallback with cost and latency budgets.
• Tech Stack: AI-Assisted Software Delivery Claude Code GitHub Copilot pytest Code Review Automation
• Challenges: 30% Test Coverage Increase: Used Claude Code for implementation planning and test generation, with human-supervised validation ensuring generated tests met acceptance criteria.
• Challenges: PR Review Time Reduced 25%: Copilot-assisted reviews with structured output schemas for security and permission review, maintaining rollback readiness.

Developed and maintained automated test suites for data pipeline and analytics platform. Collaborated with developers to implement shift-left testing practices and improve release confidence.

Data Pipeline Validation Framework

• Built Python-based framework to validate ETL pipeline integrity, schema constraints, and data quality rules across staging and production environments.
• Python, pytest, Apache Airflow, Snowflake, AWS S3, Jenkins
• Designed validation framework, wrote core utilities for schema comparison and row-level reconciliation, integrated with Jenkins pipelines.
• Tech Stack: Python pytest Apache Airflow Snowflake AWS Jenkins
• Challenges: Production Data Incidents Cut by 60%: Automated data quality checks caught schema drift and cardinality issues before deployment.
• Challenges: Shift-Left Adoption: Enabled developers to run validation suite locally, reducing feedback loop from hours to minutes.