Data Engineer

• Responsible for defining and evolving data and artificial intelligence solution architectures, ensuring adherence to corporate guidelines, scalability, security, and integration with the company’s technology ecosystem. Works on strategic projects, modernization initiatives and end-to-end solution design.
• Define service-oriented architectures, microservices and modular monoliths.
• Define and maintain the enterprise data architecture, considering multicloud environments (Azure, AWS, GCP) and on-premises deployments.
• Build data models for Analytics products (Lakehouse) and for operational use (SQL/NoSQL).
• Apply patterns such as event-driven, hexagonal, DDD, CQRS and clean architecture.
• Design solutions for internal and external Data and AI platforms with a focus on security, versioning, documentation and reuse.
• Design scalable and efficient data pipelines using tools such as Databricks, Delta Lake, Spark and Airflow.
• Ensure scalability, high availability and fault tolerance.
• Ensure integration across heterogeneous sources (SAS, Talend, Alteryx, Teradata, Hadoop).
• Integrate multicloud (AWS, GCP, Azure) and on-premises solutions.
• Support squads and partners in strategic projects.
• Contribute to the evolution of the CI/CD pipeline and DevOps/DevSecOps practices applied to data.
• Participate in technical and governance forums, contributing best practices.
• Lead projects for high-performance multidisciplinary squads, acting as a key partner in understanding business needs and enabling their strategies through software projects.
• Interact with business areas, IT and vendors to detail scopes and schedules, address architectural designs, and plan and follow up on development, testing, infrastructure and licensing deliveries.
• Ensure the establishment of implementation and configuration plans and the fulfillment of delivery timelines and schedules defined for IT solution deployments and/or system changes and/or new system functionalities.
• Disseminate knowledge of applicable technologies and programming languages used in systems development.
• Interact with other company areas to identify and resolve system issues and needs through consistent feedback and sharing of best practices with teams and support areas.

Data Architect – Consultant

• Pipeline Development: Design, build, test and maintain scalable data pipelines (batch and streaming) and ETL/ELT processes.
• AI Infrastructure: Develop and maintain data pipelines focused on the Machine Learning lifecycle, integrating structured and unstructured data.
• Quality and Governance: Ensure data quality, integrity and security by applying governance and data curation practices for use in predictive models and large language models (LLMs).
• Performance Optimization: Monitor data flow performance and optimize complex queries to reduce costs and processing time.
• Collaboration with AI Teams: Work closely with Data Scientists and Machine Learning Engineers to understand requirements and enable large-scale data consumption.

Mid-Level Data Engineer

• Perform the complete migration of the current environment, today hosted on Databricks on Azure, to AWS, including creating a new data model and restructuring legacy pipelines and routines;
• Define and evolve the Corporate Data Platform architecture (Lakehouse);
• Ensure adherence to the target model based on AWS + Databricks;
• Define architecture standards, frameworks and best practices;
• Drive the definition of the migration strategy (waves, prioritization, dependencies);
• Migration and Modernization: Lead the modernization of the legacy Data Warehouse (Azure/DataStage → AWS/Databricks);
• Define migration approaches: Incremental vs Big Bang;
• Ensure operational continuity during the transition;
• Governance & Security: Define and implement standards for: Data governance/Access control/Data quality and lineage;
• Ensure compliance with corporate policies and LGPD (Brazilian data protection law);
• DataOps & Standardization: Structure standardized and reusable pipelines;
• Implement best practices for CI/CD for data;
• Reduce dependence on manual processes and low standardization;
• Integration and Ecosystem: Design integrations with multiple sources and on-premises systems;

Data Architect, AWS, Databricks

• Design and implement data ingestion, transformation, and delivery pipelines in modern architectures (Data Lake, Lakehouse, Bronze/Silver/Gold layers or equivalents);
• Lead the migration of traditional BI workloads and models to modern environments, preserving traceability and ensuring data quality;
• Develop solutions in Python and SQL with a focus on maintainability, testability, and performance;
• Integrate data sources using event-driven architectures (Kafka or equivalents), ensuring low latency and consistency;
• Define and document data modeling, quality, and governance standards for team adoption;
• Collaborate with software engineers, data scientists, and product teams to ensure delivered data is correct, reliable, and usable.

Data Engineer – Modernization and Data Architecture

• Traduzir demandas de negócio em problemas e soluções de engenharia de dados.
• Projetar, desenvolver e implementar pipelines de dados escaláveis e resilientes.
• Construir e manter processos de ingestão, transformação e disponibilização de dados.
• Garantir a qualidade, performance e governança das soluções implementadas.
• Participar da definição de arquiteturas e padrões de engenharia de dados.
• Atuar em conjunto com times multidisciplinares, apoiando iniciativas orientadas a dados.
• Realizar troubleshooting e otimização de processos e ambientes de dados.

Engenheiro de Dados, Pleno

• Develop, evolve, and optimize large-scale data pipelines;
• Design solutions for data ingestion, transformation, and delivery;
• Ensure efficiency, performance, and reliability of production pipelines;
• Integrate data from multiple sources and systems;
• Work on workflow orchestration and pipeline automation;
• Support incident resolution and continuous improvements of the data platform;
• Collaborate with cross-functional teams (engineering, product, and business);
• Contribute to data engineering best practices, architecture, and governance;
• Participate in technical definitions and platform evolution.

Senior GCP and Azure Data Engineer

• Define and evolve enterprise data models (conceptual, logical, and physical);
• Design data structures for modern environments (Data Lake, Lakehouse, Data Warehouse);
• Ensure modeling standards are scalable, resilient, and domain-oriented;
• Support the definition of data architecture and engineering best practices;
• Collaborate with business areas to understand needs and structure data solutions;
• Define and promote data governance, quality, and organization standards;
• Serve as a technical reference, supporting engineering and analytics teams;
• Participate in the evolution of data pipelines and structures, ensuring adherence to defined models.

Data Engineer jobs in Brasil

See all jobs on JobTailor