I am a Data Engineering Specialist with solid experience in data science, machine learning engineering, and cloud data architecture. I currently lead digital migration and modernization initiatives for data stacks on Kubernetes and Azure, and have managed large-scale big data projects for banks and consultancies. My passion is building solutions that connect data engineering and data science to drive real business impact.

• Location: Rio de Janeiro, Brazil

My career began in data science, evolved into machine learning engineering, and then into large-scale data engineering. I have experience across the entire project lifecycle: building robust pipelines, designing data architectures, automating processes, and operationalizing machine learning models.

Currently, I am responsible for leading legacy system migrations to modern cloud architectures, unifying platforms, drastically reducing processing times, and enabling advanced analytics. I am proficient with AWS, Azure, Databricks, Kubernetes, Spark, Kafka, Airflow, and more.

• Kubernetes Series:
  • Kubernetes straightforward — Architecture (Part 1)
  • Precautions and how to use Spark with Kubernetes
• All my articles:
  • sebastiao--553.medium.com

• Postgraduate in Machine Learning Engineering (FIAP)
• Bachelor’s in Control and Automation Engineering (UNIFEI)
• Certifications:
  • Astronomer Certification for Apache Airflow Fundamentals
  • Big Data on Kubernetes
  • Databricks Lakehouse Platform Essentials
  • AWS Certified Cloud Practitioner
  • Fabric Analytics/Data Engineer Associate
  • dbt Fundamentals
  • Cloud Data Engineer Bootcamp

• ECAD (2024-present) — Data Engineering Specialist:
  Leading digital migration, pipeline architecture on Kubernetes/Azure, platform modernization, and reducing SLAs for data processing.
• A3Data (2022-2024) — Senior Data Engineer:
  Big data projects on the cloud, database migration, pipeline automation, and cost optimization on Kubernetes/AWS.
• DHauz (2021-2022) — Data Engineer:
  Implemented data lakes, automated ingestion, integrated Databricks/AWS, and supported data science teams.
• Precato (2021) — Data Scientist:
  Built data infrastructure, analytical pipelines, and machine learning models for lead optimization and qualification.

• Analyzing the Violence in Rio de Janeiro [PT-BR]: link

  A descriptive analysis was made with the objective of understanding which were the main influencers of the high violence observed in Rio de Janeiro.

• Airbnb Data Analysis, Buenos Aires, Argentina [PT-BR]: link

  Descriptive analysis on the data provided by Airbnb in order to understand which neighborhoods are the best to rent according to price and location.

• Features engineering, learn interactively [EN-US]: link

  One way to improve the performance of a model is feature enginerring. In this article I show how to apply this technique in a simple interactive way.

• [AWS] Building a lambda pipeline with CDK [PT-BR] : Link

  This pipeline architecture is widely used in cases where the data is integrated all in one zone. In addition, it has 3 storage stages (bronze, silver, gold). Curious? Go to the repositor at link above.

• [GCP] Hackathon A3data [PT-BR] : link

  A very cool challenge that contemplated the treatment and analysis of a large set of data. In this repository you can see my project carried out in this competition.

• Data Collection Pipeline in Yahoo Finance [PT-BR] : link

  Back to the origins. I entered the market with many cloud-oriented solutions. With that in mind I worked on this project creating instances of Hadoop, spark, and Hive to get an experience with these tools.

• bigdata-k8s-kafka link

  Complete Big Data architecture on Kubernetes, integrating Minio, Airflow, Apache Kafka, Airbyte, Hive Metastore, Spark, JupyterHub, Delta Lake, Trino, and Superset. Provisioned via Helm and manifests, deployable locally (K3D) or on managed clouds. Ideal for scalable modern data and analytics environments.

• boiler_architecture_553 link

  Boilerplate architecture for data engineering projects, facilitating the creation of new environments with standardized infrastructure, automation, and CI/CD patterns. (See repository for details.)

• Desafio1-igiti-EDC link

  Solutions for the IGTI Cloud Data Engineering Bootcamp, focusing on data architecture, pipelines, and practical cloud computing challenges.

• tech-challager-2-cloud link

  AWS project for a data tech challenge, covering infrastructure as code, automation, and integration of cloud services.

• tech-challager-3-model link

  Technical challenge focused on data modeling and machine learning, emphasizing pipeline automation and model deployment.