Top Data Engineer Jobs in Brazil

Design and operate scalable, low-latency ETL/ELT and streaming data pipelines across ClickHouse, Postgres, Athena, and cloud sources; manage schema evolution, data modeling, sharding/partitioning, CI/CD for data, monitoring, and lineage; collaborate with engineers and data scientists to productionize ML/AI models and mentor team members.

Design, deploy, and maintain ELT/ETL data pipelines on GCP using Airflow and dbt. Build scalable data platform components (Docker, CI/CD) and partner with stakeholders to deliver analytics and ML infrastructure. Apply software engineering best practices to ensure production-grade, high-quality data solutions.

Design, build, and maintain scalable cloud-native data pipelines and ELT/ETL workflows in AWS. Develop and optimize Snowflake data models, ingest structured and unstructured data, and use Databricks/Spark to process large datasets. Implement data quality, governance, monitoring, and orchestration. Collaborate with data scientists, analysts, and engineers to support analytics, reporting, and AI/ML initiatives while optimizing performance and cost.

Design, architect, and govern scalable data platforms (data lake/warehouse). Engage stakeholders to gather requirements, implement ETL pipelines, optimize performance, and manage data ingestion using Hadoop/Spark/Kafka/Airflow and AWS tools. Support data modeling, security, and workflow orchestration.

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and optimize workloads. Ingest data from APIs, databases, cloud storage and flat files, apply dimensional modeling, enforce data quality/governance, monitor pipelines, and collaborate with cross-functional teams to deliver enterprise data solutions on Azure.

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and optimize workloads. Integrate data from APIs, databases, files, and cloud storage. Enforce data quality, governance, and security; monitor and tune performance; document pipelines; and collaborate with architects, analysts, and stakeholders to deliver enterprise data solutions on Azure.

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and optimize warehouses; ingest data from APIs, databases, cloud storage, and flat files. Apply dimensional modeling, enforce data quality and governance, monitor performance, and support production incidents. Collaborate with architects, data scientists, and stakeholders; document pipelines and best practices.

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and optimize workloads. Integrate data from APIs, databases, cloud storage and files, apply dimensional modeling, enforce data quality and governance, monitor/resolve production issues, and collaborate with cross-functional teams to deliver enterprise data solutions on Azure.

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement and optimize Snowflake objects and workloads, ingest data from APIs, databases, cloud storage and flat files, and apply dimensional modeling. Ensure data quality, governance, security, monitor production pipelines, troubleshoot performance issues, and collaborate with architects, data scientists, and stakeholders.

Design, build, and maintain scalable ETL/ELT pipelines using Python (PySpark) and Snowflake. Implement Snowflake objects and optimize warehouses, manage ingestion from APIs, databases, and cloud storage, apply dimensional modeling, enforce data quality and governance, monitor/optimize performance, and collaborate with architects and stakeholders to deliver cloud-native data solutions (primarily Azure).

Design, develop, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and optimize workloads, build ingestion frameworks from APIs/databases/cloud storage, enforce data quality/governance, monitor and tune pipelines, and collaborate with architects, data scientists, and stakeholders.

Design, build, and maintain scalable ETL/ELT data pipelines using Python/PySpark and Snowflake. Implement and optimize Snowflake objects and workloads, apply dimensional modeling, integrate structured and semi-structured sources, enforce data quality, governance, and security, and collaborate with cross-functional teams to support production operations and documentation.

Design, build, and maintain scalable ETL/ELT data pipelines using Python (PySpark) and Snowflake. Manage Snowflake objects and performance, ingest data from APIs, databases, files and cloud storage, apply dimensional modeling, implement data quality and governance, monitor/optimize workloads, support production incidents, and collaborate with architects, analysts, and stakeholders to deliver cloud data solutions (primarily on Azure).

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and performance optimizations, ingest data from APIs/databases/cloud storage, enforce data quality and governance, monitor and troubleshoot pipelines, collaborate with architects and stakeholders, and document data models and processes.

Design, develop, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Build and optimize Snowflake data platforms, implement dimensional models, ingest structured and semi-structured sources, enforce data quality and governance, monitor performance and costs, and collaborate with architects, analysts, and stakeholders to deliver production-ready data solutions.

Design, build, and maintain scalable ETL/ELT pipelines using Python/PySpark and Snowflake. Implement Snowflake objects and optimize workloads, extract and model data from varied sources, enforce data quality and governance, monitor production pipelines, and collaborate with cross-functional teams to deliver enterprise data solutions.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

Design, build, and scale GTM data pipelines, lakes, and analytics to support sales, marketing, and customer success. Ensure data integrity across GTM systems, implement batch and streaming ingestion, develop enrichment/LLM pipelines, create semantic models in dbt, and automate revenue workflows and ICP/territory scoring.

Designs, builds, and monitors scalable data pipelines and storage to support marketing models. Implements data transformations, data modeling, and quality assurance using SQL, Python, and modern data stack tools. Collaborates with analysts, data scientists, and stakeholders and enforces data governance and security compliance.

Enable AI on a Data 4.0 platform using Snowflake Cortex: build AI-ready and semantic data products, support metric intelligence and anomaly detection, define reusable Cortex patterns, ensure governance, and collaborate with analytics and platform teams.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

As a Data Engineer at Tractian, you will manage data pipelines, automate workflows, and design a unified GTM data lake to support revenue analytics and improve processes.

Design, build, optimize, and monitor scalable data pipelines and infrastructure using Python, PySpark, Airflow, AWS Glue, Snowflake, and PostgreSQL. Implement DAGs, observability, automation, and reusable connectors while collaborating with platform and AI/ML teams to enable ingestion, validation, and real-time workflows.

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

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.