Top Data Engineer Jobs in Brazil

Design and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines across AWS, Azure, and GCP. Build Spark and Delta Lake workflows for structured and unstructured data, tune performance and cloud costs, and integrate MLflow for AI model operationalization. Implement CI/CD and infrastructure-as-code deployments, enforce governance and security through Unity Catalog, and collaborate with client leaders to deliver production-ready data and AI solutions.

Design, build, and optimize scalable data pipelines and cloud-based data infrastructure. Perform data analysis and validation testing, ensure data quality, integrate systems across cloud environments, and manage client projects according to requirements, standards, and timelines. Collaborate with clients and organizational teams using Agile methodologies to deliver reliable, high-performance data solutions.

Develop and maintain data pipelines for collection, processing, cleaning, and real-time stream processing. Collaborate with stakeholders to define data requirements, improve data quality and security, and manage cloud data infrastructure using AEP, Google Cloud, AWS, Snowflake, and Airflow. Apply Python and AI expertise to enhance data processing and analysis capabilities while addressing data-related challenges and emerging technology trends.

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.

Build and maintain scalable data pipelines, ETL processes, data models, dashboards, APIs, MCPs, and RAG pipelines. Ensure data quality, optimize workflows, manage cloud data warehouses, monitor pipeline reliability, and document data processes. Collaborate with data teams and business stakeholders to translate requirements into technical solutions and enable self-service and agentic analytics.

Develop and maintain cloud-based data pipelines supporting a centralized data lake and mobile field application. Extend ETL workflows for regional expansion, troubleshoot production issues, transform data with Python and SQL, manage data warehouses, and enforce data quality. Collaborate with software and architecture teams on data modeling, contribute to data architecture, and document pipelines and integrations.

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 manual QA for a revenue and financial data platform: execute investigative testing, validate revenue calculations and APIs, analyze DB records, run regression tests, report defects, and collaborate with product and engineering while supporting future automation.

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.

Responsible for data collection to support AI model training, operating cloud infrastructure for ingestion pipelines, and collaborating to enhance data quality and efficiency.

The role involves data collection for model training, managing cloud infrastructure on GCP, and collaborating to improve dataset quality and efficiency for AI applications.

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, build, and maintain scalable ETL/ELT pipelines and cloud data architectures (Databricks, PySpark, modern warehouses). Ensure data quality, governance, performance, and integrations; collaborate with cross-functional teams; document solutions and apply data security and automation best practices.

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 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.

The Data Visualization Engineer designs and implements data models and visualizations in Power BI while ensuring data accuracy and quality for clients' decision-making processes.

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.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

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.

Join a cross-functional team building and maintaining BEES data-driven features and tooling. Contribute to backend development, test automation, DevOps practices, quality strategy, and continuous improvement while partnering with product, design, and tech stakeholders.