Data Engineer with 3+ years of enterprise experience developing robust ETL pipelines and data architectures for industry leaders including Telcel and Citi Banamex. Specialized in:

🚀 10% reduction in critical data processing times 🏗️ Enterprise data governance for Data Lakes, Warehouses, and Data Marts 🤖 500+ monthly attribute validation automation with Python ☁️ Multi-cloud expertise across AWS, Azure, and GCP 🔄 ETL/ELT pipelines handling massive real-time and batch processing

"Transforming enterprise data challenges into scalable, automated solutions"

📅 May 2024 – April 2025

• 🔄 ETL Pipeline Development: Scalable solutions using Python, Airflow, PySpark
• 🤖 Process Automation: Shell scripts for Unix/Linux critical operations
• 🎯 Database Optimization: Complex SQL queries in Oracle for validation & reporting
• 📁 Transfer Management: Secure file transfers with WinSCP and monitoring protocols

🏆 Key Achievements: 10% reduction in critical processing time, improved data load accuracy

📅 February 2023 – May 2024

• 🏗️ Data Architecture: Governance frameworks for Data Lakes/Warehouses/Marts
• 📊 Metadata Management: Standards and lineage in Teradata/Hive environments
• 📋 Technical Documentation: Unified docs and data-business mapping strategies
• 🔄 Agile Methodologies: JIRA/Confluence management in Scrum environments

📅 February 2022 – February 2023

• 🗺️ Data Mapping: Complex source-to-target matrices for banking reconciliations
• ⚙️ ETL Development: Ab Initio integration with enterprise Data Warehouses
• ✅ Validation Automation: Python scripts validating 500+ monthly attributes
• 📈 Business Reporting: Cognos Analytics reports for stakeholders

Production-ready data warehouse with Bronze, Silver, and Gold layers

• 🔧 Tech Stack: Python, T-SQL, Docker, Medallion Architecture
• 📊 Architecture: Bronze → Silver → Gold data layers
• 🎯 Impact: Optimized retail analytics for enterprise clients
• ⚡ Performance: Handles millions of CRM/ERP records

Scalable cloud-native data integration platform

• 🔧 Tech Stack: Azure Data Factory, Data Lake, Microsoft Fabric
• 🚀 Features: Multi-source ingestion, CI/CD workflows
• 📈 Scale: Enterprise-level analytics and reporting

Intelligent security monitoring and analytics platform

• 🔧 Tech Stack: AWS EC2, Python, MLFlow, Docker, MongoDB
• 🔍 Focus: Real-time threat detection and analysis
• 📊 Analytics: Security dashboards and reporting

Manufacturing Technologies Engineering Universidad Politécnica de Guanajuato | June 2015

Currently researching and implementing:

🤖 Text-to-SQL with LLMs: Natural language queries for business users 📊 dbt Semantic Layer: Modern data transformation and documentation 🎨 n8n Workflow Automation: Visual ETL pipeline orchestration 🔍 Vector Databases: Semantic search for data catalogs and metadata ⚡ MLOps Integration: MLflow for data pipeline monitoring and experimentation

English: Intermediate Level - Technical reading, documentation, and professional communication

Spanish: Native

🔥 Actively seeking new opportunities in Data Engineering or Data Analyts roles

📚 Learning: Advanced MLOps, Kafka streaming, Apache Iceberg, AI-powered data engineering

🎯 Goal: Lead enterprise data transformation initiatives and intelligent automation