• Design and implement scalable data platforms and pipelines across cloud environments • Develop reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake • Build ingestion, transformation, and curation workflows for both structured and unstructured data • Implement modern data architectures including lakehouse patterns and medallion layering • Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems • Enable data pipelines for GenAI use cases • Design scalable logical and physical data models • Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents • Apply modern architecture patterns including event-driven and streaming architectures • Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence • Enable data serving layers to support downstream systems

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake) • Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems • Design scalable logical and physical data models for analytical and operational use cases • Apply modern architecture patterns including event-driven and streaming architectures • Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence • Enable data serving layers (APIs, feature inputs, analytical endpoints) to support downstream systems

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake) • Developing reliable batch, streaming, and near-real-time pipelines • Ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC) • Establish strong data observability, including monitoring of data freshness and pipeline reliability • Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions

Role Description Projetar, desenvolver e implementar pipelines robustas, conectando dados de diversas fontes utilizando principalmente ferramentas do ecossistema AWS. - Implementar soluções de enriquecimento de dados escaláveis e eficientes utilizando o DBT para transformar dados brutos em modelos de dados personalizados e insights acionáveis para o negócio. - Gerenciar integrações, processos de transformação e automações de tarefas usando conectores de dados, APIs SQL, Python e Rest. - Utilizar SQL para consultas e manipulação de dados em bancos de dados relacionais e não relacionais. - Aplicar Spark para processamento de grandes volumes de dados em tempo real e em batch. Qualifications - AWS (Athena, S3, Glue, Lambda). - Proficiência em SQL, DBT, Python e Spark. - Experiência com ferramentas de orquestração de dados como Apache Airflow. - Conhecimentos sobre diferentes técnicas de arquitetura e modelagem de dados para fins analíticos (Ex.: Data Mesh, Data Mart, Data Lake, Data Warehouse). - Experiência com Modelagem de Dados (relacional e/ou analítico) e processos ETL. - APIs, Gateways, Micro Serviços e Containers. - Noções sobre boas práticas de engenharia de software (Documentação, testes automatizados, código limpo, monitoramento). Requirements - Possuir Certificações AWS associadas à função de Cloud Data Engineer, sendo elas: - Cloud Practitioner; - Data Engineer; - Solutions Architect; - Machine Learning Engineer. Benefits - Participação nos Lucros - Carro Corporativo - Vale Alimentação - Vale Refeição - Convênio Médico - Convênio Odontológico - Gympass - Previdência Privada - Auxílio Home Office - Allya - Acesso ilimitado a diversos cursos da nossa Universidade Localiza - Programas internos de treinamento e desenvolvimento - Desconto em compra e aluguel de veículos