Python Automation Engineer focused on infrastructure workflow automation, Jira REST APIs, engineering workflow tooling, validation evidence, and operational reporting.

My background is in aerospace and defense manufacturing engineering, where I worked on traceability, troubleshooting, validation, documentation, and repeatable technical workflows. I now use Python to turn messy operational processes into testable, inspectable, and safer automation.

• Python automation for engineering and operations workflows
• Jira and GitHub REST API integrations
• Infrastructure-style ticket classification and routing
• Dry-run/live execution workflows
• CSV, JSON, Markdown, and HTML reporting pipelines
• Audit-safe filesystem automation
• Hardware-adjacent validation and evidence capture tooling
• Internal tools with clear safety boundaries and human-review checkpoints

Public-safe FastAPI/SQLAlchemy internal workflow demo for mapping role requirements to evidence records, review actions, claim boundaries, and generated artifacts.

What it demonstrates:

• FastAPI service structure
• SQLAlchemy-backed workflow records
• evidence-to-requirement mapping
• provenance and review-action patterns
• supported / partial / unsupported claim classification
• human-review boundaries for AI-assisted outputs
• fake demo data and public-data safety checks

Why it matters:

TraceOps is my strongest internal-tools project. It models a practical problem I care about: turning scattered evidence, notes, generated outputs, and project records into structured workflow records that can be reviewed before they become claims.

Repo: https://github.com/piemasterflex111/traceops-evidence-demo

Python automation project for Jira/GitHub workflow reporting and Jira-based infrastructure ticket routing.

What it demonstrates:

• Jira Cloud REST API integration
• GitHub REST API reporting workflows
• .env secrets and config.toml runtime configuration
• nested API payload normalization
• CSV exports and processed workflow metrics
• Markdown/HTML report generation
• deterministic ticket classification
• dry-run/live execution safety
• Jira-safe labels and audit comments
• pytest coverage for client, classifier, and orchestrator logic

Why it matters:

This project shows how Python can connect to real workflow tools, normalize operational data, classify tickets, and safely plan or apply Jira updates.

Repo: https://github.com/piemasterflex111/engineering-workflow-dashboard

Safety-first Python CLI for auditing, planning, and organizing large local file trees such as OneDrive or synced cloud-drive folders.

What it demonstrates:

• recursive file and directory scanning
• CSV audit reports
• cleanup candidate classification
• quarantine workflows instead of blind deletion
• collision-safe file moves
• undo/recovery scripts
• large-folder validation

Why it matters:

This project shows practical automation around messy real-world filesystems where the first requirement is not speed, but avoiding data loss.

Repo: https://github.com/piemasterflex111/safe-file-organizer

Embedded bring-up and validation evidence project demonstrating UART logging, I2C validation, and BME280 sensor identification on an STM32F446RE Nucleo board.

What it demonstrates:

• UART evidence logging
• I2C bus validation
• BME280 chip-ID proof
• structured bring-up workflow
• repeatable hardware validation documentation
• hardware/software debugging discipline

Why it matters:

This connects my engineering validation background with software-driven evidence capture and repeatable test workflows.

Repo: https://github.com/piemasterflex111/stm32-hardware-validation-framework

• Python
• REST APIs
• Jira Cloud API
• GitHub API
• pytest
• FastAPI
• SQLAlchemy / SQLite patterns
• Bash / Linux
• PowerShell basics
• Docker
• CSV / JSON / Markdown / HTML reporting
• workflow automation
• structured debugging
• validation evidence capture

• Jira and GitHub workflow reporting
• infrastructure-style ticket routing
• Linux / Windows / Network Operations handoffs
• messy CSV and API-data normalization
• safe file cleanup and audit workflows
• validation evidence capture
• engineering workflow dashboards
• repetitive manual spreadsheet/reporting processes
• internal tools with dry-run and human-review boundaries

I am focused on roles involving:

• Python Automation Engineer
• Infrastructure Automation Engineer
• IT Automation Engineer
• Workflow Automation Engineer
• Jira Automation Engineer
• Systems Automation Engineer
• Test Automation Engineer
• Engineering Tools / Internal Tools Engineer