• Build and maintain Kpler's core datasets (vessels characteristics, companies, geospatial data). • Responsible for creating and maintaining REST APIs, streaming pipelines (Kafka Stream), and Spark batch pipelines. • End-to-end ownership of development tasks, beginning with a thorough understanding of assigned tickets and requirements. • Design and build functionality—including APIs and data processing components—ensuring code is deployed to development environments and reviewed through peer and product testing. • Writing and executing unit, integration, and functional tests aligned with defined test scenarios, while ensuring full compliance through detailed validation. • Monitoring system performance, alerts, and SLOs to ensure optimal functionality and reliability.

• Develop, enhance, and maintain ETL pipelines using Azure Data Factory (ADF) • Design and implement data models and data warehousing solutions using Azure Synapse Analytics • Optimize SQL queries and perform performance tuning on large and complex datasets • Work with Azure Data Lake, Azure SQL Database, and other Azure data services to support analytics and reporting needs • Implement data security best practices, including role-based access control (RBAC) and data masking • Collaborate with business analysts and stakeholders to gather requirements and translate them into scalable technical solutions

• Estamos constantemente em busca de Engenheiros(as) de Dados que queiram construir soluções escaláveis, trabalhar com grandes volumes de dados e atuar em projetos estratégicos envolvendo Plataformas de Dados, Cloud e IA. • Mesmo que não tenhamos uma vaga aberta no momento, ao se cadastrar em nosso Banco de Talentos, seu perfil ficará disponível para futuras oportunidades alinhadas à sua experiência, interesses e momento de carreira.

• Data Warehousing: Owning and evolving our data pipelines into BigQuery, leading efforts to improve performance, scalability, and cost-efficiency. • Data Transformation: Architecting and implementing advanced data models using DBT, establishing best practices for testing, documentation, and code quality. • Integration: Leading the design and implementation of integrations with Segment and other third-party sources, ensuring data consistency and reliability across the platform. • Project Leadership: Taking ownership of complex data projects, from gathering requirements with BI, Marketing, and Data Science teams to delivering robust, production-ready solutions. • Mentorship: Guiding and mentoring other data engineers, leading code reviews, and fostering a culture of technical excellence and continuous improvement.