Ricardo Bomfim

As the coordinator of the data engineering team, I lead the migration from a managed, event-driven architecture to a fully orchestrated, modular, and scalable Data Lake on AWS. This ongoing transformation has already resulted in a 60% reduction in cloud infrastructure costs and a 90% drop in support tickets (incidents and service requests), significantly improving the reliability, speed, and quality of data delivery.

Beyond the technical achievements, this initiative has also enhanced the strategic relevance of the data engineering team within the organization, reinforcing its role as a key enabler for data-driven decision-making.

Led key data governance initiatives, including data lifecycle management, access control restructuring, data catalog implementation, and the definition of new organizational data policies.

The lifecycle management project alone drove a 30% reduction in overall cloud costs within six months. Additional efforts significantly enhanced data democratization, improved access governance, and strengthened data traceability across the company.

Acted as the technical leader and architect for a team of data engineers tasked with building a scalable end-to-end Data Lake for a large multinational corporation.

As part of the strategic vision, I introduced Domain-Driven Design (DDD) to align data architecture with business domains, enabling clear ownership, well-defined domain boundaries, and improved cross-functional collaboration between data and business teams.

• Implemented a cost allocation and observability framework in EKS, improving cloud usage transparency and enabling optimized resource consumption across product teams.
• Designed and deployed a real-time streaming solution using Kafka and Debezium, resulting in a 60% reduction in SQL Server load for logistics-related processes.
• Led the acquisition and proof of concept of a federated query engine, cutting data warehouse maintenance efforts by 80%.
• Acted as the technical lead in the end-to-end implementation of a data catalog — from POC through tool selection and deployment — ensuring full LGPD compliance and defining data governance policies, lifecycle rules, roles, and responsibilities.

Architected, implemented, and data-modeled a Redshift-based Data Warehouse from the ground up to unify patient exam data across the organization. The solution integrated multiple data sources using AWS Spectrum, EMR, Lambda, and DocumentDB, enabling a consolidated view of each patient's clinical history.

Contributed from MVP to full production rollout, significantly reducing redundant exams and enhancing the experience for patients, physicians, and healthcare providers.

Led the design and development of a Unified Data Model (UDM) in an on-premise Hadoop ecosystem, featuring Java/Shell orchestration, Scala/Spark ELT pipelines, and a Python-based data quality layer — delivered 100% on target, enabling standardized analytics across the company.

Developed automated testing pipelines for both batch and streaming data flows, fostering a paired testing culture within the engineering team.
As a result of these initiatives, the team achieved test coverage for approximately 80% of all critical data pipelines, significantly reducing data-related incidents and increasing overall trust in pipeline reliability and data delivery.

Worked across three consulting firms in the banking and retail sectors, performing a hybrid role that combined system integration, quality assurance, and continuous improvement.
Key contributions include:

• Developed detailed project schedules and supported the integration of legacy banking systems.
• Planned, developed, and executed functional and automated tests in mission-critical environments.
• Delivered internal and external technical training, accelerating team onboarding and system adoption.
• Worked with Oracle, SQL Server, Visual Studio, and MS Project, using Java, PL/SQL, and SQL.
• Identified process improvements and business opportunities, contributing to contract expansion and increased client satisfaction.