Top Data Engineer Jobs in Rio de Janeiro

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.

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.

Design, build, and maintain scalable data pipelines and architectures; ensure data quality and reliability; enable self-service analytics; collaborate with Data Science, Data Ops, and product teams; troubleshoot and optimize data systems; document and improve data workflows and infrastructure.

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.

Senior UI developer responsible for designing and building responsive React-based UIs and dashboards for cybersecurity analytics. Develop backend services and APIs in Python, integrate security data sources, optimize SQL Server/PostgreSQL reporting, collaborate with stakeholders, influence architecture, and mentor junior developers.

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.

The Senior Data Engineer will manage SQL databases, focusing on optimization, performance tuning, and security while ensuring alignment with business objectives and mentoring team members.

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.

Build and maintain scalable, high-performance data pipelines and platforms using Spark/Scala/Python. Own end-to-end projects, collaborate with product and global teams, mentor junior engineers, implement testing and performance tuning, and contribute to the technical roadmap.

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.

Operate and support ELT pipelines, design and maintain data warehouse and semantic-layer models, create BI data models and leadership reporting, troubleshoot data issues, prepare data for AI workflows, and collaborate with stakeholders to improve analytics infrastructure and recurring reporting processes.

Design and build scalable data pipelines and distributed processing systems on Databricks. Lead technical design, develop advanced data models, champion CI/CD and IaC, ensure operational reliability, mentor engineers, and collaborate cross-functionally to support analytics and ML use cases.

Design, build, and maintain scalable ETL/ELT pipelines and cloud-native Data Lake/Warehouse solutions on Azure. Implement data integration, quality, governance, dimensional models (SCD1/2), Python-based automations, CI/CD, and monitoring. Collaborate with international stakeholders to optimize performance, enable analytics, and support FinOps and deployment for enterprise data platforms.

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.

Lead design, build, and optimize a multi-country Snowflake data platform on GCP. Build CDC ingestion pipelines, implement dbt semantic models, manage Snowflake deployments via DCM/GitHub CI/CD, enforce governance/RBAC/PII protections, optimize FinOps/performance, and mentor data engineers while aligning stakeholders across Product, Risk, BI, and regional teams.

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 and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Lead and mentor a data engineering team while owning end-to-end data platform architecture and delivery. Drive migration from Snowflake/SSIS to Azure Databricks and DBT, implement Terraform IaC, CI/CD, and scalable ETL/ELT pipelines, ensure data quality, observability, and platform stability, and collaborate with stakeholders across product, data, and business teams.

Design, build, and operate scalable data pipelines and lakehouse solutions using Azure Databricks, DBT, Snowflake and Python. Lead migrations, implement data quality and observability, support production SLAs, and mentor junior engineers while ensuring governance, cost optimization, and reliability.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.