Top Data Engineer Jobs in Rio de Janeiro

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.

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.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Build and operate scalable DataOps solutions across data engineering, cloud infrastructure, and DevOps. Develop Python, Bash, Terraform, and pipeline automation; orchestrate systems through APIs; deploy and maintain AWS infrastructure; support data modeling and documentation; establish DataOps best practices; and collaborate directly with customers to gather requirements and propose technical solutions.

Build, deploy, and maintain production-grade ML models and data pipelines in cloud environments. Perform EDA and feature engineering, monitor model performance and drift, and collaborate with engineering and business stakeholders. Contribute to AI/LLM initiatives and ensure solutions operate reliably in production.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Design, build, and maintain reliable, scalable ELT/ETL data pipelines and expand the data warehouse. Partner with technical and non-technical stakeholders to deliver analytics-ready data, support reporting, and work with cloud-based data stack technologies.

As a Senior Data Engineer at Zencore, you will leverage Google Cloud to assist clients in data transformation and application modernization, covering ETL/ELT, data warehousing, and streaming data systems.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Own and deliver end-to-end data engineering projects: design, build, test, deploy, and maintain scalable big-data pipelines and APIs. Collaborate with product and global teams, mentor junior engineers, monitor performance, and drive technical roadmap and stability improvements.

Design, build, and optimize semantic Tableau data models, dashboards, reports, and datasets. Integrate and validate Databricks data pipelines, maintain data ingestion from APIs and databases, lead Looker-to-Databricks+Tableau migration, ensure data governance, performance, and security, and collaborate with stakeholders to translate business needs into scalable BI solutions.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.

Own and scale the enterprise LookML semantic layer to deliver performant, reusable Looker data products. Translate stakeholder needs into durable models, optimize query performance, govern LookML across instances, mentor junior staff, and support conversational analytics and AI-driven queries.

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.

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