Top Data Engineer Jobs in Brazil

Work with cross-functional teams to design, build, and deploy end-to-end data solutions. Own conception and implementation of data infrastructure, pipelines, ML model integration, REST APIs, and CI/CD. Collaborate, train, and present findings; stay current on cloud and big-data technologies.

As a Data Engineer, you'll maintain and enhance a data ingestion pipeline, collaborate with data scientists and engineers, and implement features for efficiency and scalability.

Lead the design and implementation of robust data pipelines and ecosystems. Ensure scalability and reliability while mentoring team members in best practices.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Develop and support enterprise Salesforce platforms through custom CRM development or Salesforce Data Cloud implementation. Responsibilities include building Apex, Lightning Web Components, Flows, integrations, automation, AI-enabled capabilities, data models, identity resolution, harmonization, SQL transformations, and scalable activation solutions. The role also involves security governance, performance optimization, troubleshooting, code reviews, release management, technical documentation, and collaboration with US-based teams during Central Time hours.

Design and optimize high-throughput batch and streaming data pipelines, cloud warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with cross-functional teams, ensure reliability and compliance, and mentor junior and mid-level data engineers.

Leads data engineering teams and defines standards for scalable enterprise data platforms. Designs pipelines, data models, cloud infrastructure, governance, security, and monitoring across AEP, GCP, and AWS. Evaluates tools such as Airflow, Snowflake, and dbt; mentors engineers; oversees delivery; partners with analytics and AI teams; advises client stakeholders; supports pre-sales through technical scoping, estimation, and presentations.

The role involves building and managing data ingestion pipelines, collaborating with AI scientists, and ensuring efficient data collection for model training at Speechify.

Build and maintain production data pipelines and reporting services for financial, payment, settlement, and transaction data. Extend merchant reports, contribute to a self-service Reporting API, manage concurrent priorities, participate in an approximately monthly on-call rotation, and support production incidents. The role emphasizes Python, SQL, AWS, Redshift, data accuracy, autonomous ownership, distributed collaboration, and AI-assisted development workflows, with Java backend development as a complementary skill.

The Data Engineer will implement ETL/ELT solutions, design data pipelines, ensure data security, and optimize data processing systems. Responsibilities include data transformation and compliance collaboration.

Design, build, and operate scalable data pipelines, models, and products supporting analytics, operational decisions, and AI. Own data architecture, dimensional modeling, data quality, lineage, orchestration, observability, and performance. Lead the migration from Redshift to Snowflake, establish engineering standards, and partner with technical and business teams to deliver trusted data solutions.

Manage eDiscovery data-source insights, preservation operations, records, and metrics. Automate Microsoft Purview workflows using PowerShell and Microsoft Graph, build sync pipelines from Microsoft 365, Google Workspace, mobile devices, and endpoints into RelativityOne, and generate collection and processing reports. The role also supports process engineering, audit readiness, scalable data collection, and high-stakes digital forensics and litigation projects.

Owns end-to-end delivery of governed Power BI reporting solutions. Responsibilities include gathering requirements, designing dashboards and semantic models, developing complex DAX calculations, optimizing performance, implementing security, collaborating on data pipelines and quality controls, and using Git, CI/CD, and Scrum practices to deploy and support analytics products.

Designs, develops, and maintains scalable cloud data pipelines and infrastructure for analytics and machine learning. Responsibilities include ETL CI/CD, data quality, dimensional modeling, data lake and warehouse implementation, governance, workflow documentation, and technical troubleshooting. The role requires Databricks, Spark, Python, SQL, strong English communication, and experience with modern cloud data engineering stacks.

As a Data Engineer, you'll design and maintain data models and pipelines, collaborate with teams, and ensure high-quality data for analytics.

Lead a data engineering squad delivering strategic, low-latency data products on Azure. Responsibilities include designing data architectures, building Python REST APIs and Databricks/dbt pipelines, implementing Terraform infrastructure, ensuring data quality and observability, driving CI/CD, resolving production issues, and mentoring engineers. The role requires hands-on technical leadership, advanced data modeling and SQL expertise, and collaboration with business and technical stakeholders.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

Build and maintain ETL/ELT pipelines, model GTM data assets, optimize complex SQL queries, ensure data quality and governance across Snowflake/GCP, and collaborate with Data Engineers and PMs to support Salesforce-integrated revenue analytics and lead routing.

Build and own end-to-end analytics data infrastructure: design and maintain ETL/ELT pipelines, model data in dbt, govern semantic layer and metrics, implement testing and observability, partner with stakeholders for requirements, run analyses and A/B test evaluation, and leverage AI tools to accelerate engineering and analytics workflows.

Lead end-to-end development of data products: ingest, model, transform, validate, and deliver datasets for analytics and AI. Translate business requirements, ensure data quality, build scalable pipelines (SQL/Python/BigQuery), enable AI consumption, and communicate solutions to stakeholders while driving proactive improvements.

Lead the evolution of a petabyte-scale AWS data platform by designing scalable data pipelines and services with JVM languages, Spark, Python, and cloud-native technologies. Provide principal-level technical leadership, mentor engineers, guide architecture, deliver complex initiatives, and ensure operational reliability through testing, monitoring, and release practices. The role also explores GraphQL, vector databases, and AI/LLM techniques for improved data access and platform efficiency.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

Own the end-to-end production data platform supporting AI agents. Build and operate API, object-storage, and event-stream ingestion pipelines; model governed Delta Lake layers in Databricks and Unity Catalog; develop low-latency serving layers; and implement data quality, monitoring, observability, CI/CD, and Terraform-based infrastructure. Optimize performance and cost, collaborate with AI, software, and product teams, and use coding agents responsibly. This Brazil-based contract requires strong Python, advanced SQL, Azure, and production data engineering expertise.

Design, build, and maintain scalable data pipelines and workflows on GCP. Contribute across the project value chain, including infrastructure, data collection, machine learning models, REST APIs, front-end and back-end development, testing, and continuous deployment. Collaborate with diverse teams, communicate technical concepts to business stakeholders, troubleshoot complex issues, prioritize work, meet deadlines, and mentor team members.

Architect and maintain end-to-end ETL/ELT pipelines, Airflow workflows, production data models, BI reporting, and ML infrastructure. Ensure data quality through governance, monitoring, validation, anomaly detection, and root-cause analysis. Optimize SQL workloads and AWS data platforms for performance, cost, and scalability. Partner with marketing, analytics, commercial, and executive stakeholders to define metrics such as CAC, ROAS, LTV, and conversion funnels. Build internal data tools and operationalize machine learning models.