Top Data Engineer Jobs in Brazil

Lead the architecture and hands-on development of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, pipelines, orchestration, governance, observability, DataOps, CI/CD, performance, cost optimization, MLOps, and AI-readiness initiatives. Mentor analytical engineers, evaluate modern tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

Leads a data engineering team responsible for building scalable safety solutions and data platforms. Owns technology roadmaps, architecture decisions, data pipeline design, cloud systems, data quality, monitoring, CI/CD optimization, and enterprise ETL/ELT. Recruits, coaches, and develops engineers while establishing engineering standards, testing practices, reusable frameworks, and reliable batch and streaming architectures supporting analytics and machine learning.

Build and maintain scalable ETL/ELT pipelines, real-time Kafka streaming applications, Snowflake data solutions, and cloud-native AWS architectures. Optimize high-volume data processing, ensure data quality and reliability, troubleshoot production pipelines, and collaborate with cross-functional teams on business-critical e-commerce data initiatives.

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.

Designs and scales enterprise data products, AI and Generative AI solutions, MLOps platforms, data pipelines, cloud-native services, APIs, and full-stack applications. Establishes standards for data quality, governance, security, observability, and software engineering; develops CI/CD and infrastructure automation; leads proofs of concept and architecture reviews; collaborates with stakeholders; troubleshoots distributed systems; and mentors engineers. The role also includes offshore team collaboration, IT finance management, and up to 5% international travel.

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.

Design, develop, deploy, and operate scalable ELT pipelines and data architectures for payments data. Integrate diverse data sources, ensure data quality and reliability, optimize pipeline and query performance, and establish data engineering best practices. Collaborate with data scientists, analysts, engineers, product managers, and customers while mentoring teams and creating trustworthy reporting and data products.

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 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 maintains secure, scalable cloud data pipelines for insurance use cases including underwriting, risk management, fraud prevention, and customer experience. Integrates structured and unstructured data, builds data lakes and warehouses, automates ingestion and quality processes, and implements governance, lineage, and regulatory compliance. The role also develops datasets for machine learning and analytics while collaborating with data scientists, architects, and business stakeholders.

Build and maintain BI dashboards, reports, data models, and governed data marts using Looker Studio, Power BI, SQL, BigQuery, Snowflake, and dbt. Analyze data for trends and insights, troubleshoot pipelines, support cloud migration validation, and improve data quality. Collaborate with engineering and business stakeholders while exploring agentic AI and predictive analytics. Manage reporting end to end, including requirements, modeling, validation, governance, and stakeholder presentations.

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 production data pipelines and models using SQL, Python, dbt, Airflow, and BigQuery. Ensure data quality, optimize databases and dashboards, deploy datasets and reporting products, and document data systems. Collaborate with analysts and stakeholders, mentor junior engineers, apply software engineering best practices, and own complex data solutions through delivery. The role requires agile collaboration, technical leadership, and regular performance audits.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

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.

Design, build, schedule, monitor, and optimize scalable data pipelines using Apache Airflow and cloud and big-data technologies. Collaborate with analysts, scientists, and engineers to ensure reliable, secure data delivery. Troubleshoot failures, maintain data architecture, document technical decisions, lead solution implementation, and mentor team members through coaching, pair programming, and design reviews.

Build and scale Klar’s data platform, including data lakes, lakehouses, streaming and batch pipelines, and real-time ingestion. Design AWS-based infrastructure using Iceberg, S3, Glue, Redshift, Athena, and Kafka. Establish data quality, governance, monitoring, and reliability practices while partnering with engineering, analytics, and business teams on AI/ML, fraud detection, reporting, reconciliations, and regulatory use cases.

Build and maintain data models, ETL/ELT pipelines, and reliable streaming integrations for high-volume telecom and customer data. Develop transformations in dbt, orchestrate workflows with Airflow, write optimized SQL, and implement data quality, monitoring, documentation, and reconciliation processes. The role partners with the Central Data team to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.