Top Data Engineer Jobs in Brazil

The Senior Data Engineer will design and optimize data pipelines from SAP to Databricks, focusing on CDC and stream processing. Responsibilities include managing Kafka components, CI/CD pipelines, and ensuring data quality while collaborating with various teams.

The Senior Software Engineer II will design, implement, scale, and maintain production systems, focusing on backend development, while collaborating with the Measurement team and mentoring others.

Design, build, and maintain scalable ELT pipelines and modern data architectures. Develop and optimize transformations with dbt and SQL, implement infrastructure as code with Terraform, and support web integrations (Node.js, TypeScript, GraphQL, React). Improve data reliability and monitoring, participate in an on-call rotation, collaborate with US-based teams, and promote data engineering best practices.

Join a cross-functional team building and maintaining BEES data-driven features and tooling. Contribute to backend development, test automation, DevOps practices, quality strategy, and continuous improvement while partnering with product, design, and tech stakeholders.

Lead architecture and implementation of scalable cloud-native data platforms and medallion-tiered pipelines. Optimize ETL/ELT, enforce data governance, security, and CI/CD/IaC. Mentor engineers, drive adoption of managed/serverless cloud services, integrate BI tools, and lead client-facing technical consulting and architectural decisions.

Backend-focused full-stack engineer building scalable, data-driven applications, APIs, integrations, and data pipelines. Work includes cloud-hosted services, containerized deployments, frontend work with modern frameworks, and contributing to AI-powered features, architecture, and cross-team delivery.

As a Software Engineer for Speechify, you will manage data collection and build data ingestion pipelines to support model training operations, collaborating with the AI team to enhance data quality and scale.

Lead data engineering strategy and architecture, design scalable batch and streaming data platforms, drive AI-centric data initiatives, implement CI/CD and IaC, improve data quality/observability/governance, mentor engineers, and deliver large cross-functional production initiatives.

Design, build, and operate analytics-ready data systems and production data pipelines. Integrate external APIs, run batch ELT into data warehouses, model data for reporting, implement orchestration (Airflow) and transformations (dbt), ensure monitoring and incident response, mentor engineers, and collaborate closely with analytics stakeholders to improve platform scalability, quality, and documentation.

The Backend/Data Software Engineer will develop scalable backend services, design data pipelines, and optimize database systems while collaborating with product and analytics teams.

As a Senior Data Engineer at Zencore, you will leverage Google Cloud to assist clients in data transformation and application modernization, covering ETL/ELT, data warehousing, and streaming data systems.

The Data Platform Engineer will design and implement ETL pipelines, develop APIs for data retrieval, and manage scalable storage, contributing to backend engineering efforts and data product development.

Build and maintain scalable, high-performance data pipelines and platforms using Spark/Scala/Python. Own end-to-end projects, collaborate with product and global teams, mentor junior engineers, implement testing and performance tuning, and contribute to the technical roadmap.

Own and deliver end-to-end data engineering projects: design, build, test, deploy, and maintain scalable big-data pipelines and APIs. Collaborate with product and global teams, mentor junior engineers, monitor performance, and drive technical roadmap and stability improvements.

Design, build, and maintain scalable data pipelines, ETL processes, and data models; ensure data quality and consistency; work with cloud data platforms and BI tools to create dashboards and data products; collaborate with analysts, data scientists, and stakeholders to translate business needs into technical solutions.

The Data Analyst will design interactive dashboards, model data, perform ETL processes, and collaborate with teams to enhance data-driven decisions using Power BI.

The Senior Machine Learning Engineer at AB InBev will design and implement ML systems and pipelines to drive AI's mission for the Growth Group.

Design, build, and optimize semantic Tableau data models, dashboards, reports, and datasets. Integrate and validate Databricks data pipelines, maintain data ingestion from APIs and databases, lead Looker-to-Databricks+Tableau migration, ensure data governance, performance, and security, and collaborate with stakeholders to translate business needs into scalable BI solutions.

Design, build and maintain scalable, observable end-to-end data pipelines on AWS. Ingest data from REST APIs, transform and load into data lakes/warehouses, implement orchestration and monitoring, and explain technical solutions to stakeholders.

Design, build, and operate an end-to-end data product supporting Sales across Europe. Develop and maintain Azure-based data pipelines, implement reporting layers (e.g., Power BI), ensure production readiness with monitoring and documentation, and partner with Sales, Finance, Data Scientists, and analysts to deliver reliable, scalable data solutions.

Responsible for data collection to support AI model training, operating cloud infrastructure for ingestion pipelines, and collaborating to enhance data quality and efficiency.

The role involves data collection for model training, managing cloud infrastructure on GCP, and collaborating to improve dataset quality and efficiency for AI applications.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.