Data & Backend Engineer for Analytics, Automation, and Decision Systems
I help teams and organizations turn raw data into reliable systems that support analytics, optimization, and automated decision-making.
My work focuses on designing and building data pipelines, analytical models, and backend APIs that are production-ready, well-structured, and easy to evolve. I typically work on projects where data quality, scalability, and long-term maintainability matter.
This GitHub profile is structured as a proof of execution, showcasing how I approach real problems end-to-end.
I usually get involved when a project needs structure, not just scripts.
- End-to-end data pipelines (ingestion → validation → modeling → consumption)
- Analytics-ready and ML-ready datasets
- Analytical data models designed for growth and change
- Automation, testing, and monitoring from day one
- APIs that expose data, logic, or optimization results
- Systems that combine data + algorithms + real constraints
- Clean separation between data, business logic, and infrastructure
- Reliable data flows between services
- Data quality rules, schemas, and validation layers
- Reproducible pipelines and documented transformations
- Systems designed to survive team changes and evolving requirements
- I design systems, not one-off solutions
- I prioritize clarity, structure, and documentation
- I build with the assumption that someone else will maintain it
- I favor simple, explicit designs over clever but fragile ones
Most of my projects start as MVPs and are designed to scale without rewrites.
These projects represent real-world system design work — not demos.
They focus on data architecture, backend systems, and operational decision logic.
Data Architecture & Analytics Engineering Case Study
- Problem: Many analytics platforms fail because the data model was never intentionally designed.
- Solution: The same dataset implemented across 3NF, Data Vault, Star Schema, and Galaxy Schema, with automated pipelines and data quality checks.
- What this demonstrates:
- Strong analytical data modeling fundamentals
- Understanding of performance vs flexibility trade-offs
- Ability to design analytics foundations, not just write queries
- Typical use cases: Data warehouse design, refactoring legacy analytics systems, BI/ML foundations.
Optimization-Driven Backend System
- Problem: Operational decisions like routing and cost optimization are often manual or rigid.
- Solution: A backend API that computes optimized fuel routing strategies using geospatial data and constraint logic.
- What this demonstrates:
- Production-ready backend architecture
- Algorithmic decision modeling
- Containerized, testable systems
- Typical use cases: Logistics optimization, cost modeling engines, internal APIs for operational decisions.
| Category | Technologies |
|---|---|
| Languages | Python, SQL, R, C# |
| Data & Analytics | PySpark, Databricks, PostgreSQL, PostGIS, dbt, Pandas, Power BI |
| Orchestration & Reliability | Dagster, Airflow, scheduling, monitoring, data quality checks |
| Backend & Infrastructure | Django, FastAPI, Docker, REST APIs, microservices |
| Cloud (practical experience) | Azure (Databricks, Storage), AWS (Lambda, S3, RDS) |
- 3+ years building production data systems
- Experience across: Data Engineering, Applied ML support, and Backend development.
- Strong mathematical foundation: BSc in Mathematics. This allows me to move comfortably between data, logic, and systems, depending on what the project needs.
I’m open to freelance projects, contract roles, and short- or mid-term engagements. If you need help building or restructuring data pipelines, analytics foundations, or data-driven backend systems, feel free to reach out.
- GitHub: 26-jorge-01
- LinkedIn: Jorge Andrés Ibáñez
- Email: jonan0804@gmail.com