Top Data Engineer Jobs in Rio de Janeiro

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.

Design, build, and maintain ELT/ETL pipelines and modular dbt models in Snowflake. Implement automated data quality tests, enforce Git/CI-CD workflows, monitor platform operations, resolve pipeline incidents, collaborate with BI and data science teams, and document data lineage and cataloging standards for a multi-country fintech analytics platform.

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.

As a Data Engineer, you'll maintain and enhance a data ingestion pipeline, collaborate with data scientists and engineers, and implement features for efficiency and scalability.

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.

Own end-to-end data engineering projects and build scalable ELT pipelines using Snowflake, dbt, and Airflow. Responsibilities include performance tuning, dimensional modeling, data warehousing, BI data preparation, platform modernization, automation, risk identification, and mentoring engineers. The role also supports future AI/ML data capabilities and requires strong independent delivery in a post-acquisition SaaS data environment.

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.

Build and maintain production data pipelines and reporting services for financial, payment, settlement, and transaction data. Extend merchant reports, contribute to a self-service Reporting API, manage concurrent priorities, participate in an approximately monthly on-call rotation, and support production incidents. The role emphasizes Python, SQL, AWS, Redshift, data accuracy, autonomous ownership, distributed collaboration, and AI-assisted development workflows, with Java backend development as a complementary skill.

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.

Design, build, and operate scalable data pipelines, models, and products supporting analytics, operational decisions, and AI. Own data architecture, dimensional modeling, data quality, lineage, orchestration, observability, and performance. Lead the migration from Redshift to Snowflake, establish engineering standards, and partner with technical and business teams to deliver trusted data solutions.

Designs, develops, and maintains scalable cloud data pipelines and infrastructure for analytics and machine learning. Responsibilities include ETL CI/CD, data quality, dimensional modeling, data lake and warehouse implementation, governance, workflow documentation, and technical troubleshooting. The role requires Databricks, Spark, Python, SQL, strong English communication, and experience with modern cloud data engineering stacks.

Lead a data engineering squad delivering strategic, low-latency data products on Azure. Responsibilities include designing data architectures, building Python REST APIs and Databricks/dbt pipelines, implementing Terraform infrastructure, ensuring data quality and observability, driving CI/CD, resolving production issues, and mentoring engineers. The role requires hands-on technical leadership, advanced data modeling and SQL expertise, and collaboration with business and technical stakeholders.

Build production LLM agents, multi-step workflows, MCP servers, evaluation and observability systems, and document-extraction pipelines. Architect AI-ready semantic data layers over Snowflake and dbt, develop Python ingestion and Airflow workloads, and implement retrieval, embeddings, governance, security, and reliable agent operations. Lead technical design, mentor peers, and advise clients in an embedded consulting engagement.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

Design, build, and optimize high-throughput batch and streaming data pipelines, cloud data warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with technical and product teams, ensure reliable and compliant production systems, and mentor junior and mid-level data engineers.

Build and own end-to-end analytics data infrastructure: design and maintain ETL/ELT pipelines, model data in dbt, govern semantic layer and metrics, implement testing and observability, partner with stakeholders for requirements, run analyses and A/B test evaluation, and leverage AI tools to accelerate engineering and analytics workflows.

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.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

Own the end-to-end production data platform supporting AI agents. Build and operate API, object-storage, and event-stream ingestion pipelines; model governed Delta Lake layers in Databricks and Unity Catalog; develop low-latency serving layers; and implement data quality, monitoring, observability, CI/CD, and Terraform-based infrastructure. Optimize performance and cost, collaborate with AI, software, and product teams, and use coding agents responsibly. This Brazil-based contract requires strong Python, advanced SQL, Azure, and production data engineering expertise.

Design and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines across AWS, Azure, and GCP. Build Spark and Delta Lake workflows for structured and unstructured data, tune performance and cloud costs, and integrate MLflow for AI model operationalization. Implement CI/CD and infrastructure-as-code deployments, enforce governance and security through Unity Catalog, and collaborate with client leaders to deliver production-ready data and AI solutions.

Develop and maintain data pipelines for collection, processing, cleaning, and real-time stream processing. Collaborate with stakeholders to define data requirements, improve data quality and security, and manage cloud data infrastructure using AEP, Google Cloud, AWS, Snowflake, and Airflow. Apply Python and AI expertise to enhance data processing and analysis capabilities while addressing data-related challenges and emerging technology trends.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

Build and operate Kubernetes-based data security services across public cloud, private cloud, and on-premises environments. Develop deployment, infrastructure, configuration, observability, and operational automation; troubleshoot complex production issues across applications, networking, and cloud platforms; improve scalability and reliability; support on-call operations; and contribute to technical initiatives, tooling, runbooks, and engineering best practices.