Top Data Engineer Jobs in Brazil

Backend-focused full-stack engineer building scalable, data-driven applications, APIs, integrations, and data pipelines. Work includes cloud-hosted services, containerized deployments, frontend work with modern frameworks, and contributing to AI-powered features, architecture, and cross-team delivery.

Design, build, and operate analytics-ready data systems and production data pipelines. Integrate external APIs, run batch ELT into data warehouses, model data for reporting, implement orchestration (Airflow) and transformations (dbt), ensure monitoring and incident response, mentor engineers, and collaborate closely with analytics stakeholders to improve platform scalability, quality, and documentation.

Design, build, and maintain scalable ELT pipelines and modern data architectures. Develop and optimize transformations with dbt and SQL, implement infrastructure as code with Terraform, and support web integrations (Node.js, TypeScript, GraphQL, React). Improve data reliability and monitoring, participate in an on-call rotation, collaborate with US-based teams, and promote data engineering best practices.

Build and operate scalable DataOps solutions across data engineering, cloud infrastructure, and DevOps. Develop Python, Bash, Terraform, and pipeline automation; orchestrate systems through APIs; deploy and maintain AWS infrastructure; support data modeling and documentation; establish DataOps best practices; and collaborate directly with customers to gather requirements and propose technical solutions.

Design, build, and optimize high-throughput batch and streaming data pipelines, cloud data warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with technical and product teams, ensure reliable and compliant production systems, and mentor junior and mid-level data engineers.

The Software Engineer will manage data collection and the ingestion pipeline, improve cloud infrastructure, and collaborate with AI scientists on large-scale data projects.

Build, deploy, and maintain production-grade ML models and data pipelines in cloud environments. Perform EDA and feature engineering, monitor model performance and drift, and collaborate with engineering and business stakeholders. Contribute to AI/LLM initiatives and ensure solutions operate reliably in production.

Design, build, and maintain scalable data pipelines and architectures; ensure data quality and reliability; enable self-service analytics; collaborate with Data Science, Data Ops, and product teams; troubleshoot and optimize data systems; document and improve data workflows and infrastructure.

Design, build and maintain scalable, observable end-to-end data pipelines on AWS. Ingest data from REST APIs, transform and load into data lakes/warehouses, implement orchestration and monitoring, and explain technical solutions to stakeholders.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Senior UI developer responsible for designing and building responsive React-based UIs and dashboards for cybersecurity analytics. Develop backend services and APIs in Python, integrate security data sources, optimize SQL Server/PostgreSQL reporting, collaborate with stakeholders, influence architecture, and mentor junior developers.

Design, build, and maintain reliable, scalable ELT/ETL data pipelines and expand the data warehouse. Partner with technical and non-technical stakeholders to deliver analytics-ready data, support reporting, and work with cloud-based data stack technologies.

Design, deploy, and operate open-source data infrastructure (Trino, Spark, Pinot, Druid) on Amazon EKS. Build production-ready, scalable platforms using IaC (Terraform, Helm), containerization, and Kubernetes, while troubleshooting complex customer environments and delivering bespoke solutions.

As a Senior Data Engineer at Zencore, you will leverage Google Cloud to assist clients in data transformation and application modernization, covering ETL/ELT, data warehousing, and streaming data systems.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Build and maintain scalable, high-performance data pipelines and platforms using Spark/Scala/Python. Own end-to-end projects, collaborate with product and global teams, mentor junior engineers, implement testing and performance tuning, and contribute to the technical roadmap.

Own and deliver end-to-end data engineering projects: design, build, test, deploy, and maintain scalable big-data pipelines and APIs. Collaborate with product and global teams, mentor junior engineers, monitor performance, and drive technical roadmap and stability improvements.

Design, build, and optimize semantic Tableau data models, dashboards, reports, and datasets. Integrate and validate Databricks data pipelines, maintain data ingestion from APIs and databases, lead Looker-to-Databricks+Tableau migration, ensure data governance, performance, and security, and collaborate with stakeholders to translate business needs into scalable BI solutions.

Operate and support ELT pipelines, design and maintain data warehouse and semantic-layer models, create BI data models and leadership reporting, troubleshoot data issues, prepare data for AI workflows, and collaborate with stakeholders to improve analytics infrastructure and recurring reporting processes.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.

Lead design, build, and optimize a multi-country Snowflake data platform on GCP. Build CDC ingestion pipelines, implement dbt semantic models, manage Snowflake deployments via DCM/GitHub CI/CD, enforce governance/RBAC/PII protections, optimize FinOps/performance, and mentor data engineers while aligning stakeholders across Product, Risk, BI, and regional teams.

Own and scale the enterprise LookML semantic layer to deliver performant, reusable Looker data products. Translate stakeholder needs into durable models, optimize query performance, govern LookML across instances, mentor junior staff, and support conversational analytics and AI-driven queries.

Build and maintain data enrichment pipelines, curate and label datasets, resolve data quality issues, prototype APIs/tools, collaborate with ML/product teams, and document reproducible workflows for large, messy industrial data.