Top Data Engineer Jobs in Brazil

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, 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.

Own end-to-end data pipelines (ingest, dbt transformations, BigQuery warehousing) and consolidate the analytics stack into a single source of truth. Build and maintain dbt models with tests and documentation, establish data quality and observability, own product analytics and instrumentation, partner with GTM and marketing on reporting, and prepare for high-volume/streaming ingestion.

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).

The Research Engineer role involves building and improving deep learning systems, focusing on data quality, dataset and RL environment design, and collaborating with researchers to meet high standards for AI applications.

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