- Technical leadership and guidance of a cross-functional team to develop data engineering solutions using Azure and DevOps practices.- Design and implementation of scalable and secure data architectures, including Azure Synapse, Azure Data Factory, and Azure Databricks.- Establishment of CI/CD pipelines with Azure DevOps to ensure continuous integration and delivery of data services.- Analysis and optimization of ETL/ELT processes to maximize performance and efficiency of data operations.- Led the migration of legacy systems to Azure-based data platforms, driving digital transformation.- Implementation of data governance policies to ensure compliance, security, and data integrity.

- Implementation of Business Intelligence solutions to support strategic decisions, encompassing data collection, development, and management.- Development and management of data pipelines using Azure Data Factory.- Coordination of a team of data analysts and developers, fostering data exploration and analysis to deliver valuable insights to the business unit.- Management and organization of corporate data to ensure accuracy and availability.- Development of policies and procedures for data governance, including the establishment of coding conventions and naming conventions for databases.- Development and enhancement of the operational model of the Universo (credit card), including integration of payment schemes such as MasterCard, SEPA, and Swift.

- Technical leadership and technology recommendation, refinement, and specification of business needs across various projects maintained within the scope of the Sales Force Audits group in the pharmaceutical industry.- Architecture of data lake solutions with Hadoop (Cloudera) to support large volumes of sales audit data.- Development and maintenance of data pipelines using SSIS (Integration Services) for integrating sales audit data.- Creation of pixel-perfect reports using Power BI and data modeling with SSAS Tabular.- Implementation of CI/CD processes for all back-ends and reporting tools, ensuring continuous and reliable delivery of critical insights.- Automation of tasks and administration scripts in a Linux environment using shell scripting for process optimization and operational cost reduction.

- Developed ETL processes in the construction of Data Mart and Data Warehouse for pharmaceutical audits such as DDD - Drugs Distribution Data, MDTR - Monitor Distribution Tracker, PMB - Pharmaceutical Market Brazil and CUP- Close-up Market. - Responsible for analysis of data quality, multidimensional modeling, maintenance of procedures, and relational systems of parameterization and performance with tools: SQL Server, SSIS, and SSAS.- Enhanced and maintained analysis service cubes using SSAS.- Provided advanced ETL services, supporting the data warehouse environment.- Led the development of data models and data warehouse schema design supporting business intelligence reporting and analysis.- Performed software review, evaluation, recommendation, design, and implementation for ETL management tools.- Provided direction and support of a multi-tiered self-service BI reporting program.Key achievements- Effectively migrated the database of Pharma Web System tool, from SQL Server 2005 to the 2016 version. PWS allowed web configuration of cube parameters processed by the company, which generated a personalized service with more flexibility and autonomy for clients to customize the analysis services.

- Design, construction, and implementation of Business Intelligence solutions based on specifications using QlikView, SSIS, SSRS, and Tableau.- Administration of Business Intelligence solutions.- Execution of database queries and conducting analyses.- Translation of business requirements into implementable designs.- Collaboration with Key Accounts to understand, analyze, and improve/automate existing business reports.- Development of SQL queries, creation, and maintenance of complex SQL code and stored procedures that populated and updated data models.- Creation and maintenance of ETL processes extracting data from various databases to support data models.

- Acquired data from primary and secondary sources, maintaining databases and data systems.- Conducted data modeling to design efficient data structures and optimize query performance.- Identified and defined new opportunities for process improvement through data analysis and business intelligence solutions.