Top Data Engineer Jobs in Rio de Janeiro

The Senior Data Engineer will design and maintain scalable cloud-native data pipelines, develop APIs, implement advanced analytics, manage AI workflows, and enhance data operations collaboratively.

As a Data Engineer, you will design and maintain data products that support game performance tracking, create integrations with APIs, and enhance data platform reliability and performance.

Join the data platform team to build and maintain cloud data infrastructure on GCP using Terraform. Ensure platform reliability, performance, monitoring, cost optimization, and collaborate with data engineers. Proactively identify improvements, security issues, and communicate status to technical and non-technical stakeholders in an international environment.

Lead end-to-end development of data products: ingest, model, transform, validate, and deliver datasets for analytics and AI. Translate business requirements, ensure data quality, build scalable pipelines (SQL/Python/BigQuery), enable AI consumption, and communicate solutions to stakeholders while driving proactive improvements.

Lead a data engineering team and own end-to-end data platform architecture, migrations (Snowflake/SSIS to Azure Databricks and dbt), scalable pipelines, Terraform IaC, CI/CD, observability, and stakeholder collaboration to deliver production-ready, high-quality data solutions.

Build scalable analytics applications and interactive visualizations. Develop backend data pipelines in Python and frontend interfaces in TypeScript. Transform business data into dashboards, improve data quality and performance, collaborate with product and design, and use AI-assisted tools to accelerate development. Hands-on, execution-focused role for remote work in Canada or LATAM.

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.

Lead architecture and scaling of the enterprise data platform. Build and modernize serverless, production-grade data pipelines with Airflow, dbt, Python, and AWS. Integrate Fivetran, Redshift, and Sigma, implement observability/testing, define warehouse models, and mentor teams while driving data governance and cross-functional alignment.

As a Staff Data Engineer, you'll design and implement governance frameworks, optimize data costs, build reusable templates, and ensure data quality and compliance using GCP.

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.

Serve as technical advisor integrating Databricks with data privacy and governance tools to ensure LGPD compliance. Configure access, metadata, and security; guide data discovery, classification, and scanning; design data lake/warehouse architectures; document workflows; troubleshoot and support cross-functional teams on data integrity and compliance.

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.

Design, build, and operate scalable data pipelines and cloud solutions, focusing on monitoring and automation while working with AWS services.

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.

Design, develop, deploy, and maintain Power BI reports and dashboards (including mobile), optimize performance for large datasets, implement incremental refresh, manage dataset/report versions, use Power BI API and PowerShell for automation, write and run data queries, and test reports for accuracy.

The role involves designing and implementing scalable applications, building backend services, developing ETL processes, and database management using Python and various technologies.

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 and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data 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.

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.

The Senior Software Engineer II will design, implement, scale, and maintain production systems, focusing on backend development, while collaborating with the Measurement team and mentoring others.

Design, build, and maintain scalable ELT pipelines and modern data architectures. Develop and optimize transformations with dbt and SQL, implement infrastructure as code with Terraform, and support web integrations (Node.js, TypeScript, GraphQL, React). Improve data reliability and monitoring, participate in an on-call rotation, collaborate with US-based teams, and promote data engineering best practices.

Backend-focused full-stack engineer building scalable, data-driven applications, APIs, integrations, and data pipelines. Work includes cloud-hosted services, containerized deployments, frontend work with modern frameworks, and contributing to AI-powered features, architecture, and cross-team delivery.

Lead data engineering strategy and architecture, design scalable batch and streaming data platforms, drive AI-centric data initiatives, implement CI/CD and IaC, improve data quality/observability/governance, mentor engineers, and deliver large cross-functional production initiatives.

Design, build, and operate analytics-ready data systems and production data pipelines. Integrate external APIs, run batch ELT into data warehouses, model data for reporting, implement orchestration (Airflow) and transformations (dbt), ensure monitoring and incident response, mentor engineers, and collaborate closely with analytics stakeholders to improve platform scalability, quality, and documentation.