Top Data Engineer Jobs in Brazil

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

Lead a data engineering squad delivering strategic, low-latency data products on Azure. Responsibilities include designing data architectures, building Python REST APIs and Databricks/dbt pipelines, implementing Terraform infrastructure, ensuring data quality and observability, driving CI/CD, resolving production issues, and mentoring engineers. The role requires hands-on technical leadership, advanced data modeling and SQL expertise, and collaboration with business and technical stakeholders.

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.

Lead end-to-end development of data products: ingest, model, transform, validate, and deliver datasets for analytics and AI. Translate business requirements, ensure data quality, build scalable pipelines (SQL/Python/BigQuery), enable AI consumption, and communicate solutions to stakeholders while driving proactive improvements.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

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.

Build and operate Kubernetes-based data security services across public cloud, private cloud, and on-premises environments. Develop deployment, infrastructure, configuration, observability, and operational automation; troubleshoot complex production issues across applications, networking, and cloud platforms; improve scalability and reliability; support on-call operations; and contribute to technical initiatives, tooling, runbooks, and engineering best practices.

Own end-to-end data engineering projects and build scalable ELT pipelines using Snowflake, dbt, and Airflow. Responsibilities include performance tuning, dimensional modeling, data warehousing, BI data preparation, platform modernization, automation, risk identification, and mentoring engineers. The role also supports future AI/ML data capabilities and requires strong independent delivery in a post-acquisition SaaS data environment.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Build production LLM agents, multi-step workflows, MCP servers, evaluation and observability systems, and document-extraction pipelines. Architect AI-ready semantic data layers over Snowflake and dbt, develop Python ingestion and Airflow workloads, and implement retrieval, embeddings, governance, security, and reliable agent operations. Lead technical design, mentor peers, and advise clients in an embedded consulting engagement.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Own the reliability, release, and customer-facing correctness of core data products. Manage end-to-end product releases, build proactive data quality monitoring, perform root-cause analyses, respond to data incidents, coordinate fixes with Engineering and Data Science, run QA tests and Looker/BI releases, investigate discrepancies versus customer first-party data, and partner with Commercial to design customer-facing data solutions.

Build and deploy production ML solutions for fraud detection, AML and claim automation. Implement data pipelines from client extracts to the data warehouse, develop/tune/test models, automate business tasks in C#, report KPIs, and engage with clients and stakeholders.

Lead development and maintenance of data services and pipelines, design efficient data models, improve architecture and monitoring, build ingestion/processing and provisioning processes, ensure data quality/security, design APIs, and collaborate with business teams to create data products.

Own technical delivery and reliability of cloud-native data platform environments on AWS/Azure for managed clients. Implement and maintain IaC and CI/CD, monitor and resolve production pipeline issues, collaborate with architects and stakeholders, produce runbooks and documentation, mentor peers, and contribute reusable IaC and automation for Elastic Platform Operations.

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.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

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 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.

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 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.

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 operate cloud-native, petabyte-scale data pipelines and platforms using Scala and Spark. Optimize distributed processing, ensure reliability and performance, deliver production-quality code, collaborate in agile teams, support deployments/monitoring, and drive cost/performance improvements.