Top Data Engineer Jobs in Brazil

Designs, develops, tests, and maintains scalable data pipelines and ETL/ELT solutions across cloud platforms, warehouses, and lakehouses. Profiles and validates source data, implements quality controls, monitoring, lineage, error handling, and automated testing, and optimizes SQL and stored procedures. Investigates pipeline failures, resolves data issues, documents technical processes, and collaborates with data owners, architects, security teams, and platform engineers while following secure data handling practices.

Design and build scalable cloud-based data solutions, develop ETL pipelines for data warehouses and lakes, ensure data quality, optimize storage and retrieval, and collaborate with data architects, modelers, product managers, analysts, and stakeholders. The role also guides junior data engineers and applies AWS, Databricks, Python, PySpark, SQL, Airflow, and data modeling technologies.

Develop and maintain data pipelines, applications, APIs, ingestion routines, and integrations within John Deere’s Enterprise Data Lake and Databricks ecosystem. Support workforce management and labor relations systems, enterprise databases, automated testing, monitoring, and CI/CD pipelines. Collaborate with global Agile teams and stakeholders in English, promote software and data engineering best practices, and deliver hybrid on-site support in Indaiatuba, Brazil.

Build and maintain production dbt pipelines, Snowflake data marts, ingestion workflows, semantic layers, and data quality checks supporting reporting, analytics, and AI features. Translate procurement logic into reliable dimensional models, optimize warehouse performance, protect PII, maintain FastAPI services, and document data lineage. Collaborate with engineering, product, and customer-facing teams while supporting customer data onboarding and communicating data definitions and tradeoffs.

Build and maintain production dbt pipelines, Snowflake data marts, ingestion workflows, and customer-facing reporting services. Translate procurement logic into tested dimensional models, implement data quality and reconciliation checks, manage semantic metrics and PII boundaries, optimize warehouse performance and cost, and maintain FastAPI services. Collaborate with engineering, product, and customer teams while documenting lineage, supporting data onboarding, and using disciplined software development practices.

Manage and improve Power BI reports, dashboards, semantic models, SSIS packages, ETL workflows, data pipelines, data warehouses, and OData integrations. Monitor scheduled jobs and refreshes, troubleshoot failures, optimize SQL and processing performance, maintain data quality, and support reliable enterprise data integration. Collaborate with technical and business teams to enable data-driven decision-making.

Build and maintain scalable data pipelines, Snowflake data warehouse solutions, dimensional models, and reliable datasets for analytics and reporting. Partner with cross-functional stakeholders and data analysts to translate business needs into data products, optimize queries and models, support Power BI dashboards, and strengthen engineering practices including testing, documentation, CI/CD, and data quality monitoring.

Owns credit bureau furnishing operations and the data pipelines supporting accurate customer account reporting. Responsibilities include building and debugging SQL and Python workflows, investigating discrepancies, resolving reporting defects, performing reconciliations, developing tests and monitoring, and partnering with Customer Service, Compliance, and Engineering. The role requires documenting data lineage, translating reporting requirements into technical rules, and ensuring verified, repeatable corrections.

Own credit bureau furnishing operations and the data pipelines supporting them. Build and debug SQL transformations, Python workflows, and reporting processes; investigate discrepancies, resolve defects, reconcile outputs, and implement automated tests, monitoring, and alerts. Partner with Customer Service, Compliance, and Engineering to address disputes, translate reporting requirements, validate fixes, and document data lineage and procedures.

Develop and optimize scalable data pipelines, ETL/ELT processes, data architectures, integrations, and migration solutions using Databricks and AWS. Responsibilities include migrating notebooks, jobs, pipelines, storage, tables, streaming checkpoints, and secrets; implementing data quality, governance, security, and validation controls; optimizing performance and costs; documenting solutions; and collaborating with analytics managers and business stakeholders in an Agile pod.

Designs, builds, and maintains scalable ETL/ELT pipelines, data models, storage solutions, and data workflows, primarily using AWS. Ensures data integrity, governance, security, observability, reliability, and performance across structured and unstructured data systems. Collaborates with product, engineering, analytics, and machine learning teams to support data-driven initiatives and optimize analytical and operational data infrastructure.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Build and maintain data pipelines, integration scripts, AI-powered workflows, and automation systems using Databricks, SQL, Python, and Git. Collaborate with nontechnical stakeholders to translate operational needs into technical solutions, monitor data integrity, improve workflow reliability, and eliminate manual processes through automation and AI-assisted development.

Build and maintain scalable data pipelines using Python and Airflow; transform, model, and validate data for downstream analytics users. Implement monitoring, logging, alerting, testing, and quality controls. Optimize cloud data systems for performance and cost, contribute to architecture and infrastructure-as-code decisions, and document processes. Collaborate with analytics engineers, analysts, and data scientists while supporting reliable enterprise data warehouses.

Own FinOps and data initiatives focused on optimizing Databricks workloads, pipelines, clusters, storage, and cloud costs. Build executive dashboards, monitor spending, detect anomalies, establish governance, and improve showback, chargeback, forecasting, and unit economics. Partner with engineering, finance, product, cloud, and operations teams to guide cost-efficient architecture and ROI-based decisions.

Investigate and resolve enterprise data incidents using SQL and MS SQL Server; monitor Airflow, Azure Data Factory, and SQL Agent pipelines; troubleshoot Tableau dashboards and data warehouses; perform controlled data corrections; automate investigations with Python or PowerShell; and coordinate incident resolution through Jira Service Management with engineering, IT, and business teams.

Build and maintain high-volume ETL/ELT pipelines using Python, SQL, Airflow, Azure Data Factory, and SQL Server/PostgreSQL. Design data ingestion, metadata, chunking, and embedding pipelines for AI and RAG systems. Optimize queries, dimensional models, warehouses, and lakehouses while enforcing data quality and governance. Collaborate with product owners, data scientists, and IT Operations to resolve production issues and maintain Git, Jira, and CI/CD standards.

Build semantic models, analytical views, and production ETL/ELT pipelines; optimize SQL queries and schemas; implement governance, security, data quality, monitoring, and anonymization; and support AI, RAG, and agentic AI workflows. The role requires strong production data engineering experience, dimensional modeling, dbt, database security, query tuning, and advanced English for direct client collaboration.

Develops data integration and analytics solutions for financial loss reporting. Partners with stakeholders, product owners, SMEs, and technology teams to document requirements, validate business rules, support source-to-target mapping and UAT, certify data products, resolve data quality issues, and improve dashboards, reports, data platforms, and downstream data consumption.

Designs and deploys scalable data pipelines and ETL/ELT processes using Azure and Databricks. Develops Python, PySpark, and SQL solutions, builds relational and analytical data models, optimizes pipeline performance, and ensures data quality. Serves as a technical SME for vendor developers through code reviews, quality standards, delivery oversight, and collaboration with product owners and business stakeholders.

Build and maintain scalable ETL/ELT pipelines, data transformations, models, integrations, and cloud-based data platforms. Ensure data quality through testing and monitoring, troubleshoot performance issues, collaborate with technical and business stakeholders, participate in code reviews, document solutions, and improve platform reliability and efficiency.

Designs, develops, and supports scalable data pipelines, integrations, data models, warehouses, and enterprise data products. The role integrates internal, cloud, API, and vendor data while ensuring quality, governance, performance, and reliability. Responsibilities include stakeholder collaboration, technical leadership, Agile project support, vendor partnership, and continuous improvement of data architecture and platform capabilities.

Build and own an enterprise lakehouse data platform from the ground up, including architecture, ingestion, transformation, entity resolution, pipelines, infrastructure as code, and platform services. Establish engineering standards, mentor team members, and ensure the platform supports analytics, machine learning, and business intelligence. Partner directly with client leadership to translate business needs into technical solutions and present architectural decisions.

Develop and maintain Power BI dashboards, reports, semantic models, and data solutions using SQL, Snowflake, and SAP HANA. Partner with stakeholders to define KPIs, business requirements, and data models; coordinate enterprise data onboarding; troubleshoot issues across source systems, data platforms, semantic layers, and reporting. Improve data quality, governance, consistency, visualization practices, and reusable analytics solutions while collaborating with distributed Agile teams.

Own and improve a Snowflake-based enterprise data platform in a regulated healthcare environment. Responsibilities include operating Snowflake, managing Fivetran/HVR and custom ingestion pipelines, building dbt models, supporting AWS S3 and Kubernetes workflows, improving data quality and observability, resolving major incidents, administering access controls, supporting Tableau dependencies, documenting operations, and mentoring engineers.