Data Engineering | Data Architecture | Data foundations for AI
I work at the intersection of data, technology, and business.
I’m a technology professional focused on data engineering, data architecture, and data platforms, with a foundation in software development and database systems.
My experience combines technical depth and business understanding. I started my career in software development and database administration, which gave me a solid grounding in systems and data management. Over time, I expanded into data platforms and large-scale data initiatives, taking on program and delivery responsibilities in projects related to Big Data and machine learning-based products.
I’ve worked in roles ranging from hands-on technical work to executive-level positions, leading initiatives that required aligning engineering execution with business goals. This has involved working with multidisciplinary teams, engaging with clients and partners, and operating in environments with data privacy and compliance constraints.
My experience spans both startup and corporate environments, including founding a software development company, which later became part of an international group.
I take end-to-end ownership of the challenges I work on, with a strong focus on execution and outcomes.
I hold a degree in Information Systems Engineering and a postdegree in Business Administration, complemented by further studies in data privacy, information security, leadership, and communication. This background allows me to connect technical decisions with business impact.
My current focus is on building modern data platforms, particularly those that are:
- Scalable and cloud-oriented
- Reliable and maintainable
- Aligned with governance and compliance needs
- Ready to support AI and advanced analytics
This involves work around:
- Data platform architecture
- Data engineering
- Data modeling and integration
- Data governance and privacy
- Data foundations for AI systems
Data Processing
- SQL
- Python
- Pandas
- SQLAlchemy
- Jupyter Notebooks
- Data modeling
- Data Warehouse
- Data Lake
- dbt
- Airflow
- Airbyte
Cloud & Infrastructure
- AWS
- Terraform
- Docker & Docker Compose
Engineering & Tooling
- Git & GitHub
- Linux
- Shell scripting
- Plain text data processing
- Soda
- AWS Solutions Architect Associate
- AWS Data Engineer Asociate
- Toon file format processing
- Spec-Driven Development
- Data for AI (RAG): document chunking, embeddings, and vector database integration (Pinecone, Weaviate)
The projects are structured to represent different layers of a data ecosystem:
- Engineering Foundations → dotfiles
- Data Platforms & Use Cases → Credit Scoring
- AI-oriented Systems → RAG Platform
A batch data platform designed to generate alternative credit scores using telecom data (CDR signals).
Key aspects:
- End-to-end data pipeline
- Behavioral data modeling
- Scalable processing design
- Business-oriented outputs
Focus:
Connecting raw data with decision-making use cases.
A Retrieval-Augmented Generation (RAG) platform aimed at improving decision support through contextual data.
Key aspects:
- Data ingestion and retrieval pipelines
- Embeddings and vector storage
- Integration with language models
- Structured knowledge access
Focus:
Supporting AI systems with reliable and contextual data.
A personal setup focused on productivity and reproducibility.
Key aspects:
- Environment consistency
- CLI-based workflows
- Development efficiency