Top Data Engineer Jobs in Brazil

The ETL Data Engineer is responsible for designing and maintaining data integration processes using Python and Azure Synapse Analytics, ensuring data accuracy for insights. Responsibilities include data warehousing, performance tuning, monitoring ETL jobs, and collaborating with cross-functional teams.

Design, build, and optimize ETL/data pipelines and Snowflake data models; implement dbt transformations and Airflow orchestration; enhance data observability; create dashboards; integrate APIs; collaborate with stakeholders to deliver analytics and reporting solutions.

Design, build, and deploy scalable data lakehouse and warehouse platforms; implement and optimize ETL/ELT pipelines (Databricks, dbt); develop data models and Medallion architecture layers; manage CI/CD and Infrastructure as Code (Azure DevOps, Terraform, CloudFormation); use cloud-native serverless offerings across major clouds; ensure data security and governance; integrate pipelines with BI tools like Tableau, Looker, and Power BI; communicate technical designs to stakeholders.

The Data Engineer role involves developing and maintaining data pipelines, ensuring data quality, and collaborating with teams to deliver solutions tailored for financial services.

Design and maintain Azure-based data pipelines, model and integrate on-prem and cloud data, build Power BI/Tableau dashboards, write complex SQL/Kusto queries, manage cloud costs, and guide teams on data best practices within Agile teams.

Lead FinOps and data initiatives to optimize Databricks workloads, monitor cloud spend, build executive dashboards, define FinOps governance, and partner cross-functionally to drive cost-efficient architecture and measurable business outcomes.

Build, maintain, and monitor data pipelines and transformations using SQL, Python, and Spark. Troubleshoot pipeline failures and data quality issues, implement schema mappings and validation rules, support metadata, lineage, and data classification, automate operational tasks, contribute to testing and monitoring, and follow security, governance, and engineering best practices while collaborating with cross-functional teams.

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, 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, 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.

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.

Build and maintain pipelines and infrastructure to ingest, clean, validate, and deliver training-ready datasets for ML. Integrate ETL/ELT processes, enforce data quality and governance, support labeling pipelines, and collaborate on architecture, code reviews, and documentation.

Design and build scalable Snowflake-based data models and pipelines using SQL and Python. Integrate data via APIs, optimize performance, and collaborate cross-functionally to support reporting and analytics while improving data engineering standards.

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

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.

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.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

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.

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 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.