Greater Barcelona Metropolitan Area
Experienced Engineer with a demonstrated history of working in the information technology and services industry. Strong engineering professional skilled in Python, FastAPI, SOLID, Clean code, Docker, Linux and Amazon Web Services (AWS).
Developed and scaled high-performance APIs and MCP servers using FastAPI and FastMCP, focusing on modular and extensible architecture. Architected RAG (Retrieval-Augmented Generation) strategies using MongoDB Atlas, optimizing high-dimensional data ingestion and vector search capabilities. Built and orchestrated complex data pipelines using Argo Workflows with Hera, streamlining automated processing on Kubernetes. Managed end-to-end microservice lifecycles via GitOps, using ArgoCD and Kustomize for robust Kubernetes deployments. Championed engineering excellence by establishing Python code quality standards based on Clean Code, SOLID, and SoC principles to ensure maintainability and scalability.
Teacher of Computational Infrastructure. The main topics of the subject are: - Virtualisation technologies: introduction and use of Hypervisors - Containerisation technologies: foundations and use of Docker - Cloud technologies: platforms and practice on AWS (IAM, EC2, ECS, Lambda, S3, RDS, DynamoDB, ...) - Data structures on Python: tuples, lists, dictionaries, namedtuples, classes, data classes, etc...
As a data engineer in the finance reporting team I am in charge of financial reports from different central European banks. I analyse the reporting requirements with internal stakeholders, select the best sources to extract the data from and finally create the tables in our datalake on which the report will be built. Tech stack used: Python, SQL, Trino, PostgreSQL, Apache Hive, Apache Airflow, DuckDB, Pydantic, pandas, SQLAlchemy, Alembic, Docker, git...
As a Python Engineer I was in charge of several credit reports of different countries, not only in Europe, but also in America, USA and Australia. With internal stakeholders I analysed the information requested by the regulator and I generated the report to be delivered using the internal platform we developed for that purpose. Tech stack used: SQLAlchemy, Alembic, Pydantic, Pandas, Airflow, SQL, Docker, PostgreSQL, Jupyter notebooks, K8S, principles like SOLID, DRY, Avoid premature optimisation, KISS, YAGNI, SoC, Boy Scout rule, etc...
As a backend engineer I'm involved on Django/DRF microservices development, sometimes from scratch , developing new components, and sometimes extending or refactoring already deployed functionality. Some of the new components have been developed using Django and Django Restframework, using Celery with RabbitMQ for managing tasks, and Django Channels for implementing Websockets approach. Other tools used: Flask, FastAPI, Kafka, Asyncio, Redis, Postgresql, Docker, Kubernetes I used VisualStudio Code as IDE with Remote Container plugin for being able to develop directly on APP container...very useful approach
Being part of the Cloud Governance Team I was focused on Cost-Optimization tasks. The idea was to deliver useful tools and platforms for helping teams at Adevinta to be more cost efficient, mostly on providers like AWS. For achieving this goal we use Python as a main language and we "spice up" the applications developed with amazing libraries like Pandas, Numpy, Scikit-learn, TensorFlow, SQLAlchemy, Flask and, of course, Boto3 ;-) Other tools used: Docker, Datadog, Splunk, Slack, Jira, Git, AWS CDK as IaC and many more...