Engenheiro(a) de Dados

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Engenheiro(a) de Dados based in Brazil. We are looking for a skilled Data Engineer to design, build, and evolve scalable, secure, and reliable data architectures. In this fully remote, full-time role, you will develop data pipelines and modern cloud-based platforms that enable efficient data integration, processing, and analytics. You will work across Data Lakes, Data Warehouses, and enterprise data ecosystems to ensure data quality, governance, security, and accessibility. Collaborating with business stakeholders and multidisciplinary technology teams, you will transform complex data into actionable insights and support data-driven decision-making. This opportunity offers a chance to contribute to innovative data initiatives, influence architectural standards, and serve as a technical reference in a collaborative, technology-driven environment.

Design, implement, and continuously improve scalable, secure, and efficient data architectures. Develop, maintain, and optimize ETL/ELT pipelines for data ingestion, integration, processing, transformation, and delivery. Build and maintain Data Lake, Data Warehouse, and analytical environments to support business intelligence and advanced analytics. Ensure data quality, traceability, security, governance, and compliance with established standards and best practices. Integrate data from multiple enterprise systems, applications, and corporate sources, ensuring consistency and reliability. Optimize data storage, processing, transformation, and consumption to improve performance, scalability, and cost efficiency. Contribute to the definition and implementation of data architecture standards, engineering practices, and modern cloud-based data strategies. Collaborate with business teams, analytics, BI, data science, and technology professionals to deliver data-driven solutions aligned with organizational needs. Provide technical guidance and act as a reference for data engineering initiatives, supporting multidisciplinary teams and knowledge sharing. Participate in the design, implementation, and continuous evolution of modern data and analytics platforms. Requirements: Bachelor's degree in Information Technology, Data Science, Engineering, Mathematics, Statistics, Physics, Chemistry, or a related field. Professional experience in Data Engineering and the design of data architecture solutions. Strong knowledge of data modeling, systems integration, and modern data management practices. Hands-on experience building, maintaining, and optimizing ETL/ELT pipelines. Advanced SQL skills and experience processing and transforming large volumes of data. Understanding of data governance, data quality, security, and traceability principles. Practical experience working with Data Lake and Data Warehouse environments. Knowledge of cloud platforms and cloud-based data services. Strong analytical and problem-solving skills, with the ability to collaborate effectively with technical teams and business stakeholders. Good communication skills, a proactive approach, and the ability to contribute to multidisciplinary projects. Nice to have: Databricks certifications or specialized training. Experience with SAP Datasphere, Cognite, or Dataiku. Python proficiency for data engineering, workflow automation, and process optimization. Experience designing or implementing modern data and analytics architectures. Previous experience in technical leadership, mentoring, or coordinating data teams. Familiarity with DataOps practices, Git, version control, and agile methodologies. Benefits: Work model: Fully remote, offering flexibility to work from Brazil. Employment type: Full-time employee position. Accessibility: The opportunity is also open to persons with disabilities (PwD). Professional development: Exposure to modern data architectures, cloud technologies, and evolving data engineering practices. Collaborative environment: Opportunities to work with multidisciplinary teams across business, analytics, and technology. Technical growth: Opportunities to contribute to architectural decisions, share expertise, and support the evolution of data platforms.