• Collaborate with data engineering teams to understand requirements and deliver efficient solutions; • Develop and maintain Azure, Databricks (PySpark), Python, Data Lake, and SQL technologies; • Use Python ElementTree for efficient XML data handling and integration of heterogeneous data; • Develop Python scripts using Pandas for manipulation and analysis of structured data; • Ensure availability, stability and continuous evolution of client systems, ensuring high performance; • Work on security and compliance for business requirements through proactive management, process automation, and continuous improvement of support services; • Maintain data quality and integrity by implementing testing and monitoring practices; • Keep comprehensive technical documentation for processes and solutions implemented; • Support corrective actions, assist with production deliveries and monitor change requests (CHGs);

• Perform the complete migration of the current environment, today hosted on Databricks on Azure, to AWS, including creating a new data model and restructuring legacy pipelines and routines; • Define and evolve the Corporate Data Platform architecture (Lakehouse); • Ensure adherence to the target model based on AWS + Databricks; • Define architecture standards, frameworks and best practices; • Drive the definition of the migration strategy (waves, prioritization, dependencies); • Migration and Modernization: Lead the modernization of the legacy Data Warehouse (Azure/DataStage → AWS/Databricks); • Define migration approaches: Incremental vs Big Bang; • Ensure operational continuity during the transition; • Governance & Security: Define and implement standards for: Data governance/Access control/Data quality and lineage; • Ensure compliance with corporate policies and LGPD (Brazilian data protection law); • DataOps & Standardization: Structure standardized and reusable pipelines; • Implement best practices for CI/CD for data; • Reduce dependence on manual processes and low standardization; • Integration and Ecosystem: Design integrations with multiple sources and on-premises systems;

• Define and implement end-to-end Data Lakehouse solutions on AWS. • Lead the automation of cloud infrastructure using Terraform. • Orchestrate large-scale performance tuning initiatives. • Establish automated Data Quality gates using AWS Glue Data Quality. • Design complex, event-driven workflows using Step Functions and Airflow. • Serve as the primary technical liaison between Data Science, BI teams, and Business Stakeholders.

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support. • Own delivery, quality, release discipline, and execution of the technical roadmap. • Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning. • Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness. • Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP. • Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap. • Push practical AI use across coding, review, testing, ops, and engineering productivity.