Top Data Engineer Jobs in Brazil

Seeking a skilled Data Engineer with Apache Airflow experience to design, build, and maintain scalable data pipelines, optimize ETL processes, and provide technical leadership while collaborating with cross-functional teams.

Lead architecture and delivery of scalable data platforms and pipelines to support analytics, ML/AI products, reporting, integrations, and customer-facing data products. Productionize model outputs, enable LLM/VLM workflows, enforce data standards, ensure observability and data quality, and partner with Data Science to turn experiments into reliable production capabilities.

The Senior Data Engineer will modernize the client's data platform, focusing on pipeline migration and implementing a dbt-based transformation framework, while collaborating with engineering teams for knowledge transfer.

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.

Design, develop, and implement Microsoft Azure Databricks data platforms and pipelines to ingest, cleanse, transform, and publish data. Assemble large datasets, collaborate with architects and leads to ensure fit-for-use data, apply coding standards, and drive process improvements.

Design, build, and operate scalable data pipelines and lakehouse solutions using Azure Databricks, DBT, Snowflake and Python. Lead migrations, implement data quality and observability, support production SLAs, and mentor junior engineers while ensuring governance, cost optimization, and reliability.

Design, build, and maintain large-scale, cloud-native data pipelines using Scala and Spark. Optimize distributed processing workloads, ensure reliability and performance at petabyte scale, contribute production-grade code, support deployments and monitoring, and collaborate within agile teams to deliver business-critical data platform solutions.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design and build scalable ETL/ELT data pipelines and a cloud-based data warehouse/lakehouse. Improve data quality, governance, and documentation. Work with stakeholders to enable analytics, BI, and machine learning by delivering clean, analytics-ready datasets and establishing data engineering best practices.

Own, optimize, and stabilize production-scale data platforms: refactor schemas, improve Airflow pipelines, build and maintain dbt models, profile and tune Snowflake, implement data quality testing, and resolve production incidents end-to-end.

The Software Engineer will develop and maintain data pipelines, manage data lakehouse solutions, ensure data reliability, and support intelligent features.

Build and maintain scalable ELT/ETL pipelines and data models for cloud cost attribution and financial reporting. Design a Cloud Economics data warehouse, perform end-to-end validation and auditing, automate cost-optimization recommendations, and deliver high-quality datasets for Product, Engineering, and Finance stakeholders.

Design, build, and maintain high-performance backend microservices and automation for the GTM Data Platform. Implement RESTful APIs, webhook-driven lead routing, and Salesforce integrations. Support AI-driven sales workflows, maintain platform codebase, and collaborate in Agile/Scrum using JIRA with distributed teams aligned to EST.

Build the data foundation and LLM analysis layer for a Creative Engine: ingest and structure multi-platform performance and sales data, run video analysis with Gemini and Claude, implement semantic search with pgvector, connect signals into a queryable intelligence layer, integrate platform APIs, and own full-stack features (Django + HTMX + Tailwind) with async pipelines (Celery + Redis).

Design, build, and maintain cloud data pipelines and Snowflake/dbt data models; develop AI-powered applications using LLMs, RAG, tool-calling, and agent workflows; create APIs and integrations; monitor performance, data quality, and costs; troubleshoot interconnected systems; apply security, governance, testing, and CI/CD best practices; partner with stakeholders to deliver scalable solutions.

Design and optimise Snowflake architectures and customer data foundations to support segmentation, personalisation, and audience activation. Build pipelines and reverse-ETL flows to Braze, Hightouch, and CDPs, implement identity resolution and consent frameworks, ensure data quality and governance, and collaborate with CRM, analytics, and marketing teams to enable multi-channel customer engagement and scalable customer 360 solutions.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitor, optimize cost and performance, migrate legacy ETL, enforce Delta Lake and multi-tenant architectures, ensure data quality/security, support customer onboarding, and maintain runbooks and CI/CD for global, SLA-driven delivery.

Own quality for the Data Platform and Analytics products: validate reports and metrics with SQL, reconcile against raw sources/APIs, implement automated data quality checks (Soda), test Airflow DAGs and Databricks jobs, automate API tests (REST/GraphQL), and communicate findings to engineering, product, and analytics teams.

The role involves designing and optimizing distributed data systems, building real-time data pipelines, and mentoring engineers while focusing on scalable data architecture and AI infrastructure.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.