Top Data Engineer Jobs in Brazil

As a Media Data Engineer, you will design and implement data pipelines using Databricks and PySpark, process social media campaign data, collaborate on data architecture, and improve data analytics capabilities for decision-making.

As a Data Engineer, design and maintain data pipelines and infrastructure, collaborate with teams for efficient data delivery, and optimize data storage and retrieval processes.

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.

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

Join the Data Foundry team as a Data Engineer, building pipelines and infrastructure for AI workflows, ensuring data quality and governance while maintaining and optimizing ETL processes.

As a Data Engineer, you will design and maintain data products that support game performance tracking, create integrations with APIs, and enhance data platform reliability and performance.

Own the enterprise data platform roadmap and architecture. Build and modernize scalable, serverless data pipelines using Airflow, dbt, Python, and Fivetran. Design and optimize Redshift-based data models and Sigma analytics, implement observability and automated testing, and mentor engineers while driving data governance cross-functionally.

Lead architecture and scaling of the enterprise data platform. Build and modernize serverless, production-grade data pipelines with Airflow, dbt, Python, and AWS. Integrate Fivetran, Redshift, and Sigma, implement observability/testing, define warehouse models, and mentor teams while driving data governance and cross-functional alignment.

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

As a Staff Data Engineer, you'll design and implement governance frameworks, optimize data costs, build reusable templates, and ensure data quality and compliance using GCP.

The role involves designing and optimizing distributed data systems, building real-time data pipelines, and mentoring engineers while focusing on scalable data architecture and AI infrastructure.

The Senior Data Engineer will design, implement, and optimize data pipelines and processes, ensuring data access and quality across systems while collaborating with backend and analytics teams.

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

Serve as technical advisor integrating Databricks with data privacy and governance tools to ensure LGPD compliance. Configure access, metadata, and security; guide data discovery, classification, and scanning; design data lake/warehouse architectures; document workflows; troubleshoot and support cross-functional teams on data integrity and compliance.

Deploy and maintain Shift's fraud detection solutions for a pod of clients: manage ETL and data pipelines, develop/test/tune/deploy models, roll out platform features, optimize production environments, engage stakeholders, report performance, and mentor junior data scientists.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Design, build, and operate scalable data pipelines and cloud solutions, focusing on monitoring and automation while working with AWS services.

Own the reliability, release, and customer-facing correctness of core data products. Manage end-to-end product releases, build proactive data quality monitoring, perform root-cause analyses, respond to data incidents, coordinate fixes with Engineering and Data Science, run QA tests and Looker/BI releases, investigate discrepancies versus customer first-party data, and partner with Commercial to design customer-facing data solutions.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

Design, develop, deploy, and maintain Power BI reports and dashboards (including mobile), optimize performance for large datasets, implement incremental refresh, manage dataset/report versions, use Power BI API and PowerShell for automation, write and run data queries, and test reports for accuracy.

Design, build, and maintain scalable ETL/data pipelines and dimensional data models in Snowflake using Coalesce.io, Airbyte, SQL Server/SSIS and Azure Data Factory. Implement Medallion architecture, ensure reliable data integrations and transformations, and collaborate with teams via Jira and GitHub to improve data modeling practices for analytics and reporting.

The role involves designing and implementing scalable applications, building backend services, developing ETL processes, and database management using Python and various technologies.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Lead development and maintenance of data services and pipelines, design efficient data models, improve architecture and monitoring, build ingestion/processing and provisioning processes, ensure data quality/security, design APIs, and collaborate with business teams to create data products.