Top Data Engineer Jobs in Brazil

Design, develop, deploy, and operate scalable ELT pipelines and data architectures for payments data. Integrate diverse data sources, ensure data quality and reliability, optimize pipeline and query performance, and establish data engineering best practices. Collaborate with data scientists, analysts, engineers, product managers, and customers while mentoring teams and creating trustworthy reporting and data products.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouse and ETL workloads into a governed medallion Lakehouse architecture, optimize Spark jobs and Delta tables, and implement data quality, lineage, and access controls with Unity Catalog. Troubleshoot production incidents, maintain Python and SQL code, and collaborate with DevOps and analytics teams on secure, observable platforms. Mentor junior engineers and contribute to Agile delivery practices.

Design, maintain, and troubleshoot ETL pipelines across development, staging, and production environments. Build data models and datasets for analytics and reporting, customize reusable pipelines for clients, write DDL and validation queries, and support platform modernization. Collaborate with clients, analysts, and stakeholders to translate requirements into SQL, configuration, and Airflow DAG changes.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Modernize legacy data warehouses and ETL workloads, optimize Spark jobs and Delta tables, and implement data quality, lineage, governance, and access controls with Unity Catalog. Develop tested Python and SQL, troubleshoot production incidents, and collaborate with DevOps, platform, and analytics teams on secure, observable data platforms.

Designs, maintains, and monitors ETL pipelines and data workflows across development, staging, and production environments. Builds data models, datasets, database DDL, validation queries, and deployment scripts for institutional analytics. Customizes reusable pipelines for clients, troubleshoots production failures, supports platform modernization, and collaborates with analysts, clients, and stakeholders to translate requirements into SQL, configuration, and Airflow DAG changes.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark and Delta performance, implement data quality and governance controls, troubleshoot production incidents, and support analytics and machine learning consumers. The role also involves writing tested Python and SQL, collaborating with DevOps and analytics teams, participating in Agile delivery, mentoring junior engineers, and using AI coding tools.

Designs, builds, maintains, and monitors ETL pipelines using SQL, Python, and Apache Airflow across development, staging, and production. Develops data models, datasets, DDL, validation queries, and deployment scripts for institutional analytics. Customizes reusable pipelines for clients, troubleshoots production failures, supports platform modernization, and collaborates with analysts, clients, and stakeholders to deliver data solutions.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production incidents. Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance while delivering well-tested Python and SQL solutions.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Modernize legacy ETL and warehouse workloads, optimize Spark jobs and Delta tables, and implement data quality, lineage, governance, and security controls. Troubleshoot production incidents, write tested Python and SQL, and collaborate with DevOps, platform, and analytics teams. The role also uses AI development tools and supports scalable, production-grade data platforms.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into a governed medallion Lakehouse, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production incidents. The role requires strong Python, SQL, data modeling, streaming, orchestration, migration, and collaboration skills, with optional Terraform, Azure DevOps, PostgreSQL, and monitoring experience.

Build and operate batch and streaming pipelines on Databricks using PySpark, Delta Lake, and medallion architecture. Modernize legacy ETL and warehouse workloads, optimize Spark jobs and Delta tables, and implement data quality, lineage, governance, and access controls. Write tested Python and SQL, troubleshoot production incidents, and collaborate with DevOps and analytics teams on secure, observable data platforms. Mentor junior engineers and participate in Agile delivery.

Designs, maintains, and supports ETL pipelines and data workflows using SQL, Python, and Apache Airflow. Builds dimensional data models and datasets for analytics, adapts shared pipelines for clients, monitors production jobs, troubleshoots failures, and maintains DDL, validation queries, and deployment scripts. Collaborates with clients and stakeholders to translate requirements into data solutions while contributing to platform modernization and cloud warehouse initiatives.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouse and ETL workloads into a governed medallion Lakehouse architecture. Develop clean Python and SQL, optimize Spark and Delta performance, implement data quality and governance controls with Unity Catalog, and troubleshoot production incidents. Collaborate with DevOps, platform, and analytics teams while supporting Agile delivery and mentoring junior engineers.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouse and ETL workloads into a governed medallion Lakehouse architecture. Develop Python and SQL solutions, optimize Spark jobs, implement data quality and governance controls, troubleshoot production issues, and collaborate with platform, DevOps, and analytics teams. Mentor junior engineers and support Agile delivery practices.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy ETL and warehouse workloads into a governed medallion Lakehouse, optimize Spark jobs and Delta tables, and implement data quality, lineage, and access controls with Unity Catalog. Develop tested Python and SQL, troubleshoot production incidents, and collaborate on observability, security, and compliance. Use AI coding tools to accelerate development. The role is fully remote and requires production-scale data platform experience.

Design and govern data models, metadata standards, taxonomies, domain boundaries, and security classifications for unstructured knowledge assets. Register and maintain data products in Databricks Unity Catalog, partner with data engineering teams on pipeline design, support privacy and legal reviews, and create repeatable modeling standards. The role also aligns knowledge data products with enterprise search and AI retrieval use cases.

Designs, maintains, and supports ETL pipelines and data workflows using SQL, Python, and Apache Airflow. Builds data models and datasets for analytics, customizes reusable pipelines for clients, monitors production jobs, troubleshoots failures, and maintains DDL, validation queries, and deployment scripts. Collaborates with clients and stakeholders to translate requirements into technical solutions and contributes to platform modernization and cloud data warehouse initiatives.

Designs, maintains, and monitors ETL pipelines across development, staging, and production environments. Builds data models, datasets, validation queries, DDL, and deployment scripts for institutional analytics. Customizes reusable pipelines for clients, troubleshoots production failures, supports platform modernization, and translates client requirements into SQL, configuration, and Airflow DAG changes while collaborating with analysts and stakeholders.

Designs, maintains, and supports ETL pipelines and data workflows across development, staging, and production. Builds data models and datasets for analytics, customizes reusable pipelines for clients, monitors production jobs, troubleshoots failures, writes database DDL and validation queries, and supports platform modernization. Collaborates with clients, analysts, and stakeholders to translate business requirements into SQL, configuration, and Airflow DAG changes.

Designs, maintains, and supports ETL pipelines and data workflows using SQL, Python, and Apache Airflow. Builds data models, datasets, validation queries, DDL, and deployment scripts for cloud data warehouses. Customizes reusable pipelines for clients, monitors production jobs, troubleshoots failures, supports platform modernization, and collaborates with analysts and stakeholders to translate requirements into technical solutions.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

Support Data & AI Governance initiatives through data analysis, curation, stewardship, documentation, cataloging, lineage, quality improvement, and policy maintenance. Use Python for analysis and automation while collaborating with technical and business stakeholders to improve data organization, accessibility, usability, and compliance with governance standards.

Design, develop, and maintain scalable cloud data pipelines and infrastructure for analytics and machine learning solutions. Collaborate with cross-functional teams to ensure data integrity, usability, and quality. Build CI/CD pipelines for ETL processes, document workflows and architectures, and provide technical troubleshooting. The role requires Databricks, Spark, Python, SQL, dimensional modeling, and Data Lake or Data Warehouse experience, with Azure, AWS, Power BI, and Unity Catalog as beneficial skills.

Designs and maintains secure, scalable cloud data pipelines for insurance use cases including underwriting, risk management, fraud prevention, and customer experience. Integrates structured and unstructured data, builds data lakes and warehouses, automates ingestion and quality processes, and implements governance, lineage, and regulatory compliance. The role also develops datasets for machine learning and analytics while collaborating with data scientists, architects, and business stakeholders.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.