Brazil
As a Data Engineer at Ford Motor Company, I specialize in designing, building, and optimizing scalable data architectures that drive strategic business decisions. My core focus is on ensuring data integrity, quality, and high availability across complex pipelines. Beyond traditional data engineering, I bring a strong background in infrastructure and DataOps practices. With hands-on experience in orchestrating and deploying applications using Kubernetes and Docker, I build resilient, automated, and highly scalable data environments. I am deeply passionate about open-source solutions, automation, and continuous improvement. I thrive on exploring new technologies to make data flows more efficient, secure, and accessible. Core Expertise & Tech Stack: • Data Engineering & Architecture: Scalable data pipelines, data modeling, ETL/ELT processes, and data quality. • Cloud & Infrastructure: AWS / GCP / Azure, DevOps practices, CI/CD. • Programming & Databases: Python, SQL, • Tools & Technologies: Docker, Kubernetes, Spark , Airflow, Kafka, dbt.
• Engineered an automated software solution to extract and process data from multiple distinct databases used in Ford's vehicle systems, optimizing automotive test analysis and ensuring high accuracy. • Architected and deployed highly scalable, reliable, and performant cloud data applications on Google Cloud Platform (GCP). • Implemented DevOps practices and automated infrastructure orchestration, significantly improving system availability and data pipeline performance. • Designed data models and optimized complex ETL/ELT pipelines to guarantee data integrity, enabling strategic decision-making and continuous improvement of embedded vehicle systems.
• Spearheaded the technical structuring of the Data Engineering department, conducting data maturity assessments and designing the foundational analytical architecture. • Architected and implemented automated, scalable ETL/ELT data pipelines, successfully modernizing the infrastructure by migrating manual data flows from heterogeneous sources. • Selected, configured, and deployed the Modern Data Stack for efficient storage and orchestration. • Established comprehensive DataOps and Data Governance guidelines to ensure the security, quality, and integrity of national tourism data.
Development of scalable data pipelines to ingest and unify multichannel fan data from major sports clubs into a centralized Customer Data Platform (CDP). Structuring the 'Single Fan View' by building complex ETL/ELT flows, integrating CRM, ticketing, and application behavior information. Orchestration of automated data flows to support AI-powered audience clustering and digital campaign activation. Implementation of DataOps and governance practices to ensure security and compliance (LGPD) in handling first-party data from millions of users.
• Developed advanced data analysis and detailed reporting for the "Brasil Participativo" platform using Python and Jupyter Notebooks. • Managed code storage and version control pipelines using GitLab, ensuring data availability and security for future analytical referencing. • Customized, configured, and managed the deployment of "Decidim" (an open-source citizen participation software), assisting in site prototyping and architecture. • Collaborated cross-functionally with UI/UX design teams, providing data-driven insights to improve user experience and platform development.
• Developed and deployed Machine Learning models aimed at optimizing internal processes and supporting strategic business decisions. • Performed comprehensive Exploratory Data Analysis (EDA) on large-scale public and internal datasets utilizing Python, Pandas, and Scikit-Learn. • Engineered automated pipelines for continuous data preprocessing, model training, and analytical report generation. • Identified high-impact business opportunities for Artificial Intelligence, proposing and implementing both supervised and unsupervised learning solutions. • Acted as a technical liaison between engineering and business units, successfully translating complex data requirements into actionable technical solutions.