Top Data Engineer Jobs in Fortaleza

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.

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.

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.