Responsible for the design and development of the new data platform. Led the creation of data pipelines orchestrated with Apache Airflow, automating complex extraction, transformation, and integration (ETL/ELT) processes. Implemented ingestion solutions from REST APIs and microservices, managing hybrid SQL and NoSQL environments. Participated in the construction of the architecture and execution of an analytical ecosystem, designed under strict standards of high availability and regulatory compliance. My management focused on engineering high-performance data pipelines orchestrated with Apache Airflow, achieving the successful transition of critical manual processes towards automated end-to-end extraction, transformation, and integration (ETL/ELT) flows. Designed and implemented an advanced layer of Regulatory Audit Dashboards and operational monitoring. These visualization tools allowed supervision of data status, ensuring process observability and regulatory compliance. Integrated internal and external modules using REST APIs and microservices oriented to Microservices architectures, allowing complete traceability of operations from diverse sources and improving data quality for subsequent analytical processes. Designed solutions aimed at automating critical processes (account management, credits, collections, and accounting movements), reducing manual error risks and improving operational efficiency.

Led the construction of data pipelines using Databricks (Delta Lake/Spark), automating balance comparisons between sources and generating discrepancy alerts. Modeled and built high-performance data pipelines in Azure Databricks/Spark, optimized for distributed processing of large volumes of information with a focus on performance, resilience, and scalability. Designed and normalized an extensible data model that represents the financial reconciliation process flow end-to-end, ensuring complete traceability, transactional consistency, and governance at each stage. Automated the multi-source reconciliation process, comparing balances between heterogeneous systems and enabling intelligent alerts for early detection of discrepancies and anomalous events. Implemented advanced validations, audits, and data quality controls, including integrity rules, continuous monitoring, and pipeline health metrics, ensuring reliable data for critical reporting and analysis.

Led the automation, extraction, integration, and transformation of information using SQL, Python, ETL/SSIS, Azure Data Factory, REST APIs, and microservices, ensuring robust, scalable, and secure pipelines from different data sources and DWH administration. Implemented and deployed robust and scalable data pipelines in Azure Data Factory, Blob Storage, and Databricks/Spark, processing large volumes of information and achieving a 90% improvement in data quality and a 50% reduction in processing times. Designed analytical and transactional data architectures on Big Data environments and Enterprise Architectures to support critical business processes, guaranteeing integrity, consistency, and 99.9% availability in production environments. Implemented a Data Governance model with OpenMetadata, documenting key processes and ensuring traceability and control of 100% of customer and portfolio modules. Developed and consumed REST APIs deployed in Docker containers and oriented to microservices, facilitating the integration of the banking core with external systems and reducing information exchange times.

Developed ETL/ELT processes and dashboards in Power BI, integrating transactional and regulatory data for audits, internal control, and risk analysis. Elaborated ASFI Regulatory reports and consolidated risk indicators in Operational Risk and Internal Control. Developed and optimized ETL/ELT processes with SQL Server Integration Services and Azure Data Factory, integrating multiple internal sources (operational, financial, and regulatory) into centralized repositories, used in auditing, monitoring, and risk analysis. Automated Power BI dashboards (DAX) and consolidated reports, allowing the tracking of more than 50 key management indicators and generating a monthly saving of 30 hours in manual reports. Administered and monitored information ingestion and transformation processes in Data Lake.

Designed, developed, and maintained critical components of the banking core, ensuring the functional integrity of transactional processes and critical information, with availability exceeding 90% in key operations. Built optimized SQL queries and ETL processes in SQL Server for the extraction and analysis of operational and financial data, contributing to the generation of strategic reports and ensuring the integrity of historical records in migration processes. Implemented automatic controls and validations in integration pipelines, reducing errors in critical processes, strengthening regulatory compliance, and ensuring consistency in information delivered to regulators. Documented and standardized data flows to ensure process traceability and reproducibility, facilitating internal and external audits. Collaborated with risk, audit, operations, and marketing teams, supporting the definition of analytical requirements for customer segmentation, behavior analysis, and product and channel monitoring. Reports generated in Power BI allowed identification of consumption patterns and optimization of commercial strategies.

Led data architecture, business intelligence, auditing, and data analysis projects, providing reliable information for strategic decision-making. My approach focuses on designing secure, efficient, and scalable solutions, combining DataOps, CI/CD, API integration, microservices, and cloud computing (Azure, Docker, Data Lake). I distinguish myself by technical leadership, analytical thinking, and a comprehensive vision, aligning data strategy with business objectives, internal control, and regulatory compliance. Developed advanced and customized analytical solutions for production, logistics, and financial processes, increasing operational traceability by 40% and reducing errors in critical data capture and registration by more than 50%, thanks to the implementation of automated flows, intelligent validations, and control structures. Implemented a comprehensive data quality framework, incorporating validation rules, automatic audits, continuous monitoring, and reliability metrics. This framework strengthened internal audit compliance, reduced reprocessing, and elevated the consistency of critical business information. Led the migration, cleansing, and modeling of historical and operational databases, ensuring efficient structures, high availability, and improved performance for advanced analytics, KPI generation, and predictive model construction. Collaborated closely with operational, administrative, and managerial areas, translating business needs into sustainable and scalable analytical solutions. Designed automatic data quality controls, reducing reprocessing by 70% and ensuring internal and regulatory compliance. Coordinated with multidisciplinary teams (risk, audit, operations, and IT) to align the data strategy with digital transformation objectives, supporting strategic decision-making of senior management. Led initiatives for optimizing cloud data infrastructure, achieving greater scalability, reducing storage costs, and improving response times.