Python Developer · Data Analysis · Automation — I build practical software for messy data, repetitive workflows and systems that need predictable behaviour. My projects span data pipelines, backend services, local AI and full-stack products.
# Build useful systems. Make failure visible. from stack import Python, SQL, FastAPI, Pandas from principles import Traceability, Review, Reliability class BuildApproach: data_quality = "Validate before trust" automation = "Make repetitive work explicit" ai_execution = "Propose → review → accept" failure_mode = "Report it; don't hide it" def objective(self) -> str: return "Useful, inspectable software." build = BuildApproach() print(build.objective())
Independent projects built to solve concrete problems. Open a case to see the engineering choices; public repositories link directly to the code. Private builds are identified honestly.
Browse all GitHub repositoriesDifferent domains. One consistent approach: understand the problem, make the moving parts visible, and build in the checks.
I build Python applications, data workflows and AI-enabled tools that move beyond a demo: they clean and validate input, store state, expose useful information, and make important decisions inspectable. My recent work ranges from CSV quality pipelines and job-search orchestration to local AI tooling and a full-stack learning platform.
Make the data trustworthy. Make the system observable. Make consequential actions reviewable.
My software work is self-directed and project-based; I am not presenting these builds as commercial client deployments. The evidence is in the repositories, the design choices, and the systems I have put together across 2025–2026.
Before focusing on software, I spent years in production and logistics. That background is not the job I am asking you to hire me for; it is the reason I pay attention to traceability, edge cases, throughput and what happens when a workflow fails. I am looking for a junior Python/backend, data analysis or automation role where I can turn that mindset into software with a team.
No percentage bars pretending to measure expertise. These groups show the tools I have used in projects and distinguish active learning from hands-on implementation.
My paid professional history is in production and commercial logistics, not software engineering. Alongside that career, I began learning web development on 21 November 2024 and have continued building practical projects across data, backend development and applied AI. The timeline distinguishes that ongoing independent development from my employed roles.
Started with HTML, CSS and JavaScript fundamentals, then expanded into Python, SQL, data cleaning and ETL, backend services, local AI and full-stack application development. The learning is evidenced by independent builds and technical decisions—not presented as formal certification or paid software employment.
Led the wringing/drying stage with a 3-operator team on production lines processing approximately 8,700 hides per week. Set up and adjusted drying machinery, performed quality checks, maintained batch and process records, and improved throughput through machine-parameter and workflow adjustments.
~19 tons/day across benzine 92/98 & diesel. Coordinated logistics, delivery scheduling, and inventory for high-volume commercial operations.
Hiring for a junior Python, data analysis or automation role? Working on a problem involving messy data, local AI or a repetitive workflow? Send me a note — I am happy to discuss the work and walk through relevant projects.