Alireza
Sani

Python Backend & Automation Engineer | Engineering & Scientific Computing I build reliable software for API integrations, webhook services, Telegram bots, data processing pipelines, and engineering calculations. With a strong background in Civil and Environmental Engineering combined with modern backend tech (Python, Go, Docker, Linux, MySQL, MongoDB), I bridge the gap between technical domain calculations and robust software architecture. What I bring to the table: • Backend & Automation: Production-grade FastAPI services, WebSockets, asynchronous pipelines, and automated alerting systems. • Engineering & Environmental Tooling: Custom scientific workflows, automated calculation sheets, structural/environmental data analysis, and numerical simulation scripts (Pandas, NumPy). • Quality & Architecture: Containerized deployments, clean code architecture, and automated test suites with pytest. How I work: We define a clear written scope and acceptance criteria first. Work is delivered i

https://www.linkedin.com/in/alireza-sani-a7b41644/?isSelfProfile=true

https://x.com/alirezaeuzf

Work experience

Self-Employed / Freelance

January 2021 – Current time

Job title

Python Backend & Automation Engineer

Work experience & achievements

- Built and shipped production Python services: FastAPI webhook APIs, async data pipelines with asyncio and WebSockets, and REST integrations against crypto exchanges (Binance, Hyperliquid). - Developed automation and alerting systems (Telegram bots, HMAC-SHA256 signed webhooks, idempotent retry handling) currently used for market monitoring and operational notifications. - Designed and maintained backend layers in Python and Go over MySQL/MongoDB, containerised with Docker and deployed on Linux. - Engineered invoice validation and verification engines with pytest coverage and modular architecture. - Built engineering analysis tooling for structural and environmental calculations (Python + Pandas/NumPy). - Own the full delivery cycle as a freelancer: scoping, acceptance checklist, milestones, source code with setup docs and verification steps.

Independent Project — Kaggle NFL Big Data Bowl 2027

October 2026 – Current time

Job title

Data Science & Analytics Project

Work experience & achievements

Independent competition project for Kaggle’s NFL Big Data Bowl 2027. Built a reproducible Python pipeline using official competition data to test whether NFL Combine offensive-line movement metrics improve prediction of rookie regular-season pass-protection outcomes. Engineered drill-level trajectory features and player-level exposure-aware outcomes; analyzed 71 offensive linemen across 24,908 pass-blocking records. Compared a beta-binomial baseline with sensor-enhanced models using forward-by-draft-class validation across three draft classes. Sensor features did not improve mean held-out negative log loss (baseline 4.098 vs. sensor 4.349); results are exploratory and do not establish that sensor metrics lack value. Prepared a Kaggle execution notebook and draft write-up; neither is currently public. No award or placing claimed.