Top Data Engineer Jobs in Brazil

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.

Designs, builds, and maintains scalable, secure data platforms and end-to-end pipelines. Responsibilities include data modeling, transformation, quality, troubleshooting, performance optimization, deployment monitoring, governance, self-service data enablement, stakeholder alignment, and evaluating new technologies. The role also contributes to strategic technical direction, data-driven culture, coding standards, and cross-team leadership.

Designs and maintains scalable, secure data platforms and end-to-end pipelines. Builds data models, transformations, quality controls, self-service datasets, and observability practices. Troubleshoots data issues, optimizes SQL and platform performance, evaluates technologies through proofs of concept, and partners with stakeholders on scalable solutions. The role also contributes to technical strategy, data governance, engineering standards, and data-driven culture while serving as a technical reference for the data engineering team.

Design, build, schedule, monitor, and optimize scalable data pipelines using Apache Airflow. Maintain reliable and secure data architecture, troubleshoot failures, document technical decisions, and provide technical leadership. Collaborate with analysts, data scientists, and engineers while mentoring team members through design reviews, pair programming, and knowledge sharing.

Build and operate scalable batch and streaming data pipelines powering AI systems. Own ingestion, storage, transformation, serving, monitoring, validation, and continuous improvement across the data lifecycle. Partner with AI engineers and researchers to translate model requirements into reliable production infrastructure, using data lakes, warehouses, relational, graph, and vector databases. Define data strategy and ensure high-quality, observable, timely data for AI models and agents.

Build and maintain scalable batch and streaming data pipelines, develop and optimize data models in a cloud warehouse, implement ELT/ETL and orchestration (Airflow/Dagster), ensure data quality and monitoring, handle sensitive data with HIPAA-aligned practices, and evolve the platform's data architecture while partnering with analytics, product, and engineering teams.

Build and maintain end-to-end data pipelines, models, data marts, and semantic layers for marketing, client, BI, product, and AI consumers. Integrate fragmented advertising and customer data across multiple platforms and tenants, manage dbt projects, ensure data quality, optimize performance, and support client-specific modeling. Use AI-assisted development workflows, collaborate cross-functionally, and deliver reliable, AI-ready data products.

Design, build and own end-to-end data solutions and infrastructure. Implement scalable pipelines using cloud and big-data technologies, optimize performance, create REST APIs and CI/CD, coach teammates, and communicate technical solutions to business stakeholders.

Own image and video dataset lifecycle and quality, build and maintain data pipelines and versioning, create internal tools for labeling and curation, coordinate with labeling teams, and improve training-data collection using model-assisted and active-learning approaches.

Build and operate reliable data infrastructure for scientific and operational use. Responsibilities include developing ingestion pipelines, orchestrated workflows, warehouse data models, integrations, testing, monitoring, alerting, documentation, and data quality investigations. The role collaborates with scientists and engineers to productionize datasets for geospatial and statistical analysis and improve data lineage, permissions, schemas, and self-service access.

Designs, builds, and maintains scalable AWS and Snowflake data platforms, pipelines, integrations, data models, and quality controls. Owns production workflows, including SAS-based healthcare claims processing, while improving monitoring, resilience, security, governance, performance, and cost efficiency. Collaborates with clients to deliver analytics and AI-ready datasets, modernize legacy processes, document solutions, and support reliable data operations.

Design and maintain the graph data layer for a fashion advisory platform: schema design, batch ingestion, probabilistic entity matching and deduplication, editorial signal weighting, openCypher query optimization (sub-3s), and cross-functional collaboration with ML and GenAI agent developers.

Develop and maintain scalable data pipelines, ETL/ELT processes, data architectures, integrations, and data quality systems using Databricks and cloud platforms. Optimize pipeline performance, query efficiency, and storage costs while implementing governance, security, and documentation standards. Collaborate with analytics managers and business stakeholders to deliver data-driven solutions. The role requires working from the Campinas office three days per week.

Lead migration from SQL Server to AWS Redshift by developing production-grade dbt models, converting legacy T-SQL procedures into modular Redshift SQL, architecting dbt projects, optimizing batch transformations, and mentoring engineers on scalable data modeling and reporting practices.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Lead lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to onboard commercial, finance, R&D, and real-world data domains. Ensure data quality, observability, lineage, governed access, masking, and row- and column-level security while enabling reliable AI-agent access to enterprise data.

Design and build multi-modal data ingestion pipelines, manage embeddings in vector and graph databases, integrate AI-assisted agents, collaborate on evaluation pipelines, and deploy/optimize scalable data services and workloads on GCP using Python-based APIs and orchestration tools.

Work with cross-functional teams to design, build, and deploy end-to-end data solutions. Own conception and implementation of data infrastructure, pipelines, ML model integration, REST APIs, and CI/CD. Collaborate, train, and present findings; stay current on cloud and big-data technologies.

Lead the design and implementation of robust data pipelines and ecosystems. Ensure scalability and reliability while mentoring team members in best practices.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Build and evolve a SaaS platform for lab testing and regulatory compliance. Responsibilities include full-stack development, systems architecture, technical trade-off evaluation, security implementation, and close collaboration with founders and customers during early-stage product development.

Develop and support enterprise Salesforce platforms through custom CRM development or Salesforce Data Cloud implementation. Responsibilities include building Apex, Lightning Web Components, Flows, integrations, automation, AI-enabled capabilities, data models, identity resolution, harmonization, SQL transformations, and scalable activation solutions. The role also involves security governance, performance optimization, troubleshooting, code reviews, release management, technical documentation, and collaboration with US-based teams during Central Time hours.

The Data Engineer will implement ETL/ELT solutions, design data pipelines, ensure data security, and optimize data processing systems. Responsibilities include data transformation and compliance collaboration.

Owns end-to-end delivery of governed Power BI reporting solutions. Responsibilities include gathering requirements, designing dashboards and semantic models, developing complex DAX calculations, optimizing performance, implementing security, collaborating on data pipelines and quality controls, and using Git, CI/CD, and Scrum practices to deploy and support analytics products.

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