• Perform data engineering tasks, including data modeling, data pipeline development, and cube development • Work with relational databases, including schema design, queries, views, store procedures, and performance optimization • Data governance and master data management • Build data pipelines that execute ETL/ELT processes to move and integrate data between data sources • Conduct DevOps activities, including code reviews, testing, alerting, documenting, retrospectives, incident analysis • Work with Azure's core data and analysis services, including Data Factory, Synapse Analytics, Data Explorer, Power BI, etc. • Build data warehouses, including the use of ETL/ELT, SQL, Data Lake, Spark, Azure Synapse, Databricks, tabular cubes.

• Plan and align the project with the Androidization strategy • Gather and validate functional and technical requirements • Design the solution architecture and data model • Automate the integration and processing of operational data • Model and automate refined tables with business rules • Implement monitoring and proactive alerts • Publish and validate the tables in the production environment • Document the full technical and functional architecture of the platform • Conduct training sessions and formalize the technical handover

• Planejar e alinhar o projeto com a estratégia de Androidização • Coletar e validar requisitos funcionais e técnicos • Desenhar a arquitetura da solução e o modelo de dados • Automatizar a integração e tratamento dos dados operacionais • Modelar e automatizar tabelas refinadas • Implementar monitoramento e alertas proativos • Publicar e validar as tabelas em ambiente produtivo • Documentar toda a estrutura técnica e funcional da plataforma • Realizar treinamentos e formalizar o handover técnico

• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and design data models and schemas that facilitate data analysis and reporting • Design, develop, and maintain scalable and efficient data pipelines and ETL processes to ingest, process, and transform large volumes of data from various sources into usable formats • Build and optimize data storage and processing systems, including data warehouses, data lakes, and big data platforms, using AWS services such as Amazon Redshift, AWS Glue, AWS EMR, AWS S3, and AWS Lambda, to enable efficient data retrieval and analysis • Implement and manage real-time data streaming architectures using AWS services like Amazon Kinesis or Apache Kafka to enable real-time data processing and analytics • Perform data profiling, data cleansing, and data transformation tasks to prepare data for analysis and reporting • Implement data security and privacy measures to protect sensitive and confidential data using AWS security services and features • Design and implement data architectures following Data Mesh principles within the AWS environment, including domain-oriented data ownership, self-serve data infrastructure, and federated data governance • Provide technical guidance and mentorship to junior data engineers, reviewing their work and ensuring adherence to best practices and standards