Data Engineer with 5+ years of experience. Good with Azure, GCP, AWS, Python, SQL, Spark, Airflow and more. Comfortable with multi-cultural environments.
Able to build and deploy data pipelines, agentic AI systems and RAGs, dashboards, as well as run and explain machine learning algorithms and data analysis tasks.
Fluent in English 🇺🇸, Spanish 🇪🇸 and Portuguese 🇧🇷.
(Studying Dutch 🇳🇱).
Tech Seals hires senior developers for Dutch companies.
• Highly skilled migrant 🇳🇱
Minsait is part of Indra: A leading global technology company based in Spain with over 57,000 employees worldwide.
• Supervised a team of 4 Data Engineers.
• Designed new Cloud Architecture which resulted in cost reduction of LLM based chatbots by 132.9X per message and a 6.8X reduction in Time to Market (TTM).
• Deployed chatbot that handled 120k monthly messages with integrated data pipeline, enabling an end to end solution with Microsoft Azure, Microsoft Fabric and Power BI.
• Migrated Terabytes of legacy, on-prem data using PySpark, Airflow and CI/CD best practices (Kimball Model).
uCloud is an award-winner, fastest growing Google Cloud Partner in LATAM.
• Deployed AI Agents using DialogFlow CX, including voice features, averaging 15k monthly messages.
• Reduced NLP workflows Time to Market (TTM) by more than 1000x with BigQuery and Gemini integrations.
• Built 10+ dashboards for internal management with Looker and BigQuery.
• Deployed APIs, Text 2 SQL solutions on serverless services.
• Coded multiple Python, serverless functions on AWS Lambda for data extraction and ingestion tasks.
• Built 15+ Dashboards with Power BI and Power Query.
• Developed mathematical modeling for project design and eliminated electrical testing on
machines due to the accuracy, saving 2-3 weeks and over 30k USD per project.
• Presented problem solutions to managers of cross-functional teams.
• RESEARCH: Coronary Artery Disease (CAD) prediction and prevention by use of Machine Learning.
• Applied Python, Data Wrangling and Data Prep techniques for Machine Learning.
• Emphasis on Machine Learning techniques for cardiovascular disease prevention.
• Learned professional management of people and business with a data-driven approach backed up by cases and articles.
• Special emphasis on customer behavior and experience within a company's services and products.
• Learned to generate business value through data using technologies like Python, R, Machine Learning, and Cloud services.
• Focused on business consulting, how to use data to gather insights and best practices.
• Learned C++ and Python for signal processing and
mathematics tools such as statistics, Fourier, and Laplace.
• Developed final IOT project on Arduino for automatic light dimming based
on current environment brightness.
Complete project from inspection to analysis, using Google's ecosystem (Cloud Storage, BigQuery, Colab). According to experts, the number of heart attacks increases when the temperature drops, more specifically below 14°C (57°F). How does this statement hold up by analyzing a sample from a particular city?
R Project with several insights and use of Classification Algorithms, such as Generalized Linear Models (GLM), Recursive Partitioning and Regression Trees (RPART). In this project I propose data-driven decisions to the HR.
Recommendation systems are a game-changer when we talk about user experience and relevant customization to drive sales and retention.
The "Real-Time Industrial Analytics with Spark and Kafka" project is a game-changer in the realm of industrial automation. By harnessing the capabilities of Apache Spark and Kafka, this innovative project paves the way for data-driven decision-making, predictive maintenance, and improved overall efficiency in the industrial sector. It exemplifies the tremendous potential of combining big data processing with real-time streaming analytics and serves as a beacon of the future of industrial analytics.
Learn how it was possible to save the manager's time 6 hours a week and improve weekly meeting duration up to 500%. (WIP)
Work in Progress.