Backend Engineering Portfolio

Hi, I'm rfransozo 👋

Senior Python Backend Engineer

I build scalable, cloud-ready systems with strong foundations in architecture, data, and operational reliability. This portfolio simulates how real backend systems evolve across a team — from APIs to infrastructure to analytics.

View Projects GitHub Profile

🐍 Python ☁️ AWS 🏗 Terraform 🐳 Docker 🐘 PostgreSQL ⚙️ GitHub Actions

About

End-to-end backend systems

Each project targets a specific responsibility found in production environments. They are intentionally connected and designed to reflect how real engineering problems evolve — not isolated tutorials.

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Backend Systems

Business logic, APIs, and clean architecture

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Cloud Infrastructure

AWS deployment, security, and scalability

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CI/CD

Automation, testing, and operational reliability

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Data Pipelines

ETL design, analytics, and business metrics

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Applied AI

Pragmatic LLM integration with RAG

Core Projects

Five connected repositories

Each repository covers a distinct engineering responsibility. Together they form a cohesive picture of how production backend systems are built and operated.

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Backend Platform

python-subscription-platform ↗

Production-grade Python backend for subscription billing, order management, and access control.

Clean architecture and domain-driven design (light DDD)
Business-oriented modeling — not CRUD-only
Authentication, authorization, and idempotent workflows

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Cloud Infrastructure

aws-python-backend-infra ↗

AWS infrastructure as code for running Python backend services in a production-like environment.

Infrastructure as Code with Terraform
Secure networking and least-privilege IAM design
Cost-aware and horizontally scalable architecture

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CI/CD Automation

python-backend-cicd ↗

CI/CD pipelines for Python backend services using GitHub Actions.

Automated testing and validation on every commit
Container build and deployment workflows
Safe, maintainable, and reviewable pipeline design

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Data and Analytics

python-data-analytics-pipeline ↗

Data pipeline transforming transactional backend data into analytical models and business metrics.

ETL design and layered data modeling
Business metric definitions: MRR, churn, LTV
Bridging backend systems with analytics consumers

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Applied AI

llm-support-assistant-backend ↗

Backend service integrating LLMs with retrieval-augmented generation (RAG) for customer support use cases.

Pragmatic LLM integration without over-engineering
Cost and latency trade-offs made explicit
Robust fallback strategies for production reliability

Tech Stack

Tools I work with

Focused on the Python ecosystem, AWS cloud, and the surrounding tooling needed to build and operate reliable backend systems.

Languages & Frameworks

Python FastAPI SQLAlchemy

Infrastructure & Cloud

AWS EC2 RDS S3 IAM VPC Terraform Docker

Data & AI

PostgreSQL ETL Pipelines RAG LLMs

DevOps

GitHub Actions CI/CD Automated Testing

Design Philosophy

How I approach engineering

These repositories are deliberate simulations of real engineering problems — not tutorials. They reflect how I think and build in practice.

Clear ownership

Every component has a defined responsibility. Ambiguity is resolved in design, not at runtime.

Explicit trade-offs

Non-goals are documented, not hidden. The reasoning behind decisions matters as much as the code.

Simplicity first

No premature complexity. Systems grow in response to real constraints, not anticipated ones.

Operability

Systems that can realistically run in production — observable, deployable, and maintainable.