Top Data Engineer Jobs in Rio de Janeiro

Build and operate a governed people-data platform supporting workforce analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, privacy controls, and reliability practices. Own the platform’s AI-readiness and infrastructure roadmap, monitor performance, manage incidents, and partner with People Analytics, HR Technology, Security, Legal, and Enterprise Data teams. Translate approved requirements into secure, production-ready technical solutions using Workday and Azure-based data technologies.

Build and maintain secure HR data pipelines, warehouses, and data marts using dbt, Python, Snowflake, BigQuery, Airflow, and AWS. Design semantic layers and ontology models for BI and AI agents, deploy Snowflake Cortex and MCP workflows, monitor pipeline reliability, enforce data quality and PII security, and document technical processes. Collaborate remotely with distributed teams aligned to EST.

Build and operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Designs and maintains Denodo semantic layers, virtualized data products, gold-layer dimensional models, and optimized data pipelines. Integrates AWS, Oracle, Redshift, APIs, Informatica, and AWS Glue; tunes queries, caching, and visualization performance for Power BI and Tableau. Provides platform support, enforces governance and security standards, manages metadata publication, and contributes to solution design, estimation, documentation, and knowledge transfer.

Designs and supports scalable AWS Glue and PySpark data ingestion pipelines from databases, files, APIs, and streaming sources into Amazon S3. Responsibilities include medallion architecture processing, data catalog management, quality controls, reconciliation, workflow orchestration, performance and cost optimization, production support, incident resolution, and governance collaboration.

Design, develop, integrate, deliver, maintain, and support data pipelines and applications. Administer PostgreSQL, Aurora, RDS, and SQL Server databases; build Databricks ingestion pipelines; architect scalable applications; troubleshoot production issues; and deliver customer-facing solutions using modern cloud, software engineering, Agile, and DevSecOps practices.

Own and operate a Snowflake-based enterprise data platform in a regulated healthcare environment. Responsibilities include production support, ingestion pipelines, dbt models, AWS S3 data-lake operations, orchestration with Argo and Kubernetes, data quality, observability, incident response, Tableau dependencies, automation, documentation, and mentoring. The role requires on-call participation, cross-functional collaboration, technical leadership, and continuous improvements to platform reliability, performance, security, and cost.

Build and maintain scalable data pipelines, transformation models, and analytical datasets for media and entertainment clients. Translate complex business requirements into robust dbt and SQL models, integrate revenue and performance data from multiple platforms, and document data assets in dbt catalogs. Collaborate with product managers, visualization leads, analysts, and technical leadership while applying AI-assisted development tools and maintaining data quality standards.

Transforms organizational data into actionable insights supporting revenue growth, operational efficiency, forecasting, and strategic planning. Responsibilities include building predictive models, developing scalable data tools, creating KPI dashboards and reports, leading end-to-end analytics projects, and presenting recommendations to senior leaders. The role partners with Finance and business stakeholders on planning and performance reviews while communicating complex findings through executive summaries and visual storytelling.

Lead enterprise data deployments across Latin America, integrating customer data into ClickHouse or warehouse-native Snowflake and BigQuery environments. Build orchestration pipelines, canonical data models, semantic layers, and AI-assisted discovery workflows. Partner directly with customer data teams, resolve complex integration challenges, optimize performance, and establish the region’s deployment playbook. Professional English and Spanish fluency are required, with regular customer travel and potential future relocation within Latin America.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

Owns end-to-end data engineering for ingestion, medallion architecture, curated datasets, and Power BI semantic layers. Builds scalable ETL/ELT pipelines, integrates ERP and CRM systems, implements data quality and governance controls, optimizes performance and cloud costs, and provides technical leadership. Requires Microsoft Fabric or Azure expertise, Azure Data Factory, advanced Power BI and DAX skills, data contract design, and experience with Bronze/Silver/Gold architectures.

Own and develop relational database and lakehouse data layers for higher-education applications and analytics. Build optimized SQL schemas, procedures, and models; develop Databricks, PySpark, Spark SQL, and dbt pipelines; implement governance, security, quality checks, and lineage; manage CI/CD database changes; document systems, resolve incidents, collaborate with architects and developers, and mentor mid-level engineers.

Designs, builds, and maintains production data pipelines and infrastructure supporting analytics, machine learning, and financial decision-making. Responsibilities include data ingestion, modeling, quality validation, workflow orchestration, database optimization, monitoring, troubleshooting, and managing scalable AWS or on-premises systems. The role collaborates with data scientists and analysts to ensure reliable access to large-scale and time-series financial data.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Develop and deploy AI-enabled applications for digital supply chain operations. Responsibilities include building frontend interfaces, APIs, PostgreSQL databases, ETL pipelines, enterprise integrations, and containerized deployments. The role implements LLMs, RAG, AI agents, predictive models, and AI workflows while optimizing SQL, documenting architectures, and supporting production operations.

Design, develop, and optimize enterprise data pipelines and lakehouse architecture using Databricks, Spark, and Delta Lake. Build ETL/ELT processes for banking data, improve cluster and query performance, establish data quality monitoring, and support regulatory compliance. Collaborate with technical and business stakeholders, document workflows, participate in code reviews, mentor junior engineers, and contribute to continuous improvement.

Designs and delivers end-to-end data engineering solutions for enterprise clients, including production implementation, data integration, SQL optimization, cloud data platforms, automated transformation, orchestration, testing, deployment, and documentation. The role also requires client-facing communication, technical presentations, solution roadmaps, proof-of-concept development, and collaboration to ensure performance, security, scalability, and reliability.

Design, develop, and optimize scalable data pipelines using Databricks, Apache Spark, SQL, Python, Scala, and Azure. Process large datasets, improve pipeline performance and reliability, implement data quality and governance practices, troubleshoot complex issues, document solutions, and collaborate with technical teams, data professionals, business stakeholders, and clients.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

Designs and evolves scalable data pipelines, warehouses, lakes, and streaming architectures. Builds ETL/ELT processes, ensures data quality and security, optimizes database performance, and translates complex requirements into technical solutions. Collaborates with data scientists and stakeholders on modeling while mentoring junior engineers. The role requires modern data stack, cloud, programming, data modeling, communication, and client-facing nearshore or offshore experience.

Designs and optimizes semantic data models, analytical views, and production ETL/ELT pipelines for AI and analytics workloads. Tunes databases for low-latency retrieval, implements RBAC, row-level security, data masking, governance, monitoring, and quality standards, and collaborates with AI engineering teams on RAG and agentic AI systems.

Designs and implements scalable data pipelines, cloud data infrastructure, dimensional models, and Data Lake/Data Warehouse solutions. Maintains data quality, governance, cataloging, lineage, and ETL CI/CD processes. Requires strong data engineering experience with Azure, SQL, Spark, Azure Data Factory, Azure DevOps, and modern data stacks, plus advanced English and international client experience.

Design and maintain relational and document-based data architectures, including MS SQL Server and MongoDB schemas, complex queries, stored procedures, indexing, and performance tuning. Build secure data pipelines and APIs, deploy schemas through automated CI/CD pipelines, resolve data quality issues, and support production software. Develop testing strategies and automation while collaborating in English across LATAM and Central Time teams.

Build and maintain SQL-based transformation logic across staging, conformed, and curated data layers on a cloud warehouse. Recreate business logic, dimensional models, reconciliation processes, and data-quality checks. Support BI consumers through governed curated datasets and participate in code reviews, CI/CD promotion, and automated regression testing. Collaborate in English with distributed engineering teams and client stakeholders.