Top Data Engineer Jobs in Brazil

Lead data engineering architecture and standards, build and operate large-scale batch and streaming pipelines, partner with product and business teams to deliver scalable data solutions, manage technical debt, and balance build vs. buy decisions to maximize analytics and platform impact.

Design, build, test, and maintain cloud-based ETL/ELT pipelines for geospatial and operational data. Implement spatial transformations and geospatial data models in BigQuery, integrate APIs and feeds, enforce data-quality controls, troubleshoot discrepancies, and produce clear technical documentation while collaborating across distributed engineering teams.

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.

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.

Design, build, and maintain scalable Databricks-based data pipelines (PySpark, SQL) to process shipment, sell-out, and market share data. Collaborate with architects and product teams, reduce technical debt, provide L3 operational support, and deliver solutions within an Agile/Scrum environment.

Design, build, and maintain a scalable open-source data lakehouse platform (S3, Apache Iceberg, ClickHouse). Develop and optimize ETL/ELT pipelines and orchestration (Temporal), ensure platform reliability and performance, collaborate with data scientists and engineers, and implement data quality, governance, and security best practices. Focus on batch processing and cloud-native microservices environments.

Design, build, and maintain ELT/ETL pipelines and modular dbt models in Snowflake. Implement automated data quality tests, enforce Git/CI-CD workflows, monitor platform operations, resolve pipeline incidents, collaborate with BI and data science teams, and document data lineage and cataloging standards for a multi-country fintech analytics platform.

Design and build multi-modal data ingestion pipelines, manage embeddings in vector and graph databases, integrate AI-assisted agents, collaborate on evaluation pipelines, and deploy/optimize scalable data services and workloads on GCP using Python-based APIs and orchestration tools.

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.

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

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.

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.

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.

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

Design, build, and maintain ETL pipelines and data transformations (Microsoft Fabric, SQL, Python, Power Query). Implement data quality checks, incremental loads, dimensional models, and documentation. Troubleshoot operational issues, support UAT, and collaborate with leads on architecture and semantic models to power analytics and reporting for the Mortgage Cadence platform.

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.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

Design, build, and optimize ETL/ELT pipelines and distributed data processing (Python/PySpark) on AWS. Implement data lake architectures, partitioning, and migrations from legacy RDBMS. Monitor pipeline health, ensure data quality and governance, support observability, and collaborate with analysts, scientists, and infra teams to deliver scalable analytics solutions.

Design, develop, and maintain scalable ETL/ELT pipelines and real-time streaming applications. Build and optimize data processing solutions with Python and Spark, implement Snowflake data warehousing, and architect cloud-native data services on AWS. Ensure data quality, performance, and reliability while troubleshooting production pipelines and collaborating with cross-functional teams.

Build and operate the data platform behind regulatory indices: source messy public and commercial data, implement idempotent medallion ingestion pipelines, design Postgres schemas and migrations, construct auditable index methodology (normalization, weighting, sensitivity testing), and run pipelines on Prefect/ECS with strong observability and test coverage.