Diego M.

As a consultant specializing in Azure data architecture, I have been responsible for designing efficient cloud solutions, optimizing costs, and improving platform performance for multiple clients. Key Achievements and Responsibilities: - Cloud Architecture Definition: Developed robust cloud architectures tailored to business needs. - Cost Reduction Strategies (CapEx/OpEx): Delivered significant cloud cost reductions by rewriting solutions using Azure Functions, optimizing both CapEx and OpEx without compromising performance. - AWS to Trino Migration: Achieved a 30% cost reduction and improved performance by migrating a client’s data warehouse from AWS to Trino. - Azure DevOps Pipelines: Designed and implemented CI/CD pipelines to streamline deployment processes. - Monitoring and Tool Selection: Defined tools and processes for efficient environment monitoring. Project Acceleration: Accelerated a client’s cloud solution by migrating their data warehouse from AWS to Trino, resulting in a 30% reduction in OpEx and enhanced performance. Skills: Databricks | Azure DevOps | Azure Data Factory | Azure Functions | Blob Storage | Trino | Azure SQL | Power BI

Worked in a data engineering team within the marketplace area, implementing scalable solutions for large volumes of data from sources like DynamoDB. Key Achievements and Responsibilities: - Data Collection and Processing: Collected data using Kafka and processed it with EMR and Spark, ensuring efficiency in handling large data volumes. - DataOps and BDD: Introduced DataOps and BDD practices to ensure high-quality production delivery. - Cost Management and Optimization: Assisted in managing resources and reducing costs in AWS. - Sustaining Practices in Production: Implemented sustaining practices for data engineering projects in production environments. - Business Process Improvements: Identified and applied improvements with the business team, providing a 360° view of the marketplace's impact on the organization, enabling more informed decision-making and strategic alignment. Skills: Trino | EMR | Spark | Airflow | Kafka | DynamoDB | CDC | BDD

Worked as a Data Engineer, responsible for designing and implementing a Big Data architecture for an international project at AB-InBev, focused on optimizing data collection and analysis processes, ensuring higher operational efficiency. Key Achievements and Responsibilities: - Architecture and Optimization: Developed the architecture with Azure Data Factory, Databricks, and Data Lake, reducing data delivery time to dashboards by 70%, replacing previous non-Spark solutions. - Preventive Maintenance System: Collected sensor data using Azure IoT Hub and Synapse Analytics notebooks for data collection, transformation, and delivery, enabling a preventive maintenance system based on AI to predict equipment failures. - Sustaining and Monitoring: Defined the data support model using Datadog, setting the foundation for new developments and continuous monitoring of the production solution. Skills: Azure Data Factory | Databricks | Spark | Data Lake | Datadog | Synapse Analytics | IoT Hub | Streaming Data | Preventive Maintenance Systems

Led data engineering initiatives through JDC Informática, managing cloud resources, implementing CI/CD pipelines, and designing data solutions in an agile environment. Key Achievements and Responsibilities: • CI/CD Pipelines for SSIS: Created a CI/CD pipeline for SSIS, including validation of the data ingestion process, reducing analysis time. • Cloud Resource Management: Managed Azure cloud resources and optimized costs. • Data Warehouse Design: Developed DW models for the Control Tower to monitor wagon movements with a 30-minute delay. • Data Processing: Built data pipelines with PySpark on Databricks and Data Factory. • ETL Processes: Designed ETL pipelines using SSIS. Skills: CI/CD Pipelines | Azure DevOps | SSIS | PySpark | Databricks | Data Factory | Data Warehouse Design | Azure Cloud Resource Management | ETL Processes | Agile Methodology

Provided technical leadership to the BI team, managing on-premises infrastructure and BI demands. Key Achievements and Responsibilities: - Team Leadership: Guided the BI team in delivering projects efficiently and effectively. - BI Demand Management: Handled requests and managed the on-premises BI environment. - Project Development: Designed, developed, and maintained BI solutions using SSIS, SSAS, SSRS, and Power BI. - Budget Process Optimization: Collaborated with management to streamline and systematize the budgeting process. Call Center BI Optimization Project: Led a project to optimize the call center’s BI processes, centralizing data from multiple sources. This included automating reports and dashboards, reducing manual reporting by 50%, and enabling quicker, data-driven decisions to enhance operational efficiency. Skills: SSIS | SSAS | SSRS | Power BI | On-premises BI Environments | Budget Systematization | Team Leadership | Agile Methodology

Contributed to BI projects in a production environment, with a focus on data integration, tool implementation, and SSIS migration. Key Achievements and Responsibilities: - BI Project Support: Provided support for BI projects in a live production environment. - QlikView and QlikSense Implementation: Assisted in the implementation of QlikView and QlikSense tools. - Data Integration Development: Developed data integration projects to streamline processes. - SSIS Migration: Migrated around 2000 SSIS 2005 packages to SSIS 2016, improving performance and modernizing ETLs, laying the groundwork for a future transition to a Big Data architecture.

Contributed to BI projects in a production environment, focusing on data integration, tool implementation, and SSIS migration. Key Achievements and Responsibilities: BI Project Support: Provided support for BI projects in a live production environment. QlikView and QlikSense Implementation: Assisted in the implementation of QlikView and QlikSense tools. Data Integration Development: Developed data integration projects to streamline processes. SSIS Migration: Migrated around 2000 SSIS 2005 packages to SSIS 2016, improving performance and modernizing ETLs, laying the groundwork for a future transition to a Big Data architecture.

Working in the financial sector at AlmaViva do Brasil - Creation and maintenance of projects using SSIS, SSAS, SSRS, and PowerBI - Migration of queries to ETL processes in Integration Services (SSIS)