Top Data Engineer Jobs in Brazil

Build and maintain cloud data pipelines, Snowflake and dbt models, data quality frameworks, and governed data platforms. Develop AI-powered applications using LLMs, RAG, prompt engineering, tool calling, and agent workflows. Create APIs and integrations, monitor system health and costs, troubleshoot interconnected systems, and apply security, testing, CI/CD, and governance practices while collaborating with technical and business stakeholders.

Build and maintain cloud data pipelines, Snowflake data models, and dbt transformations while ensuring data quality, governance, testing, and reliability. Develop AI-powered applications using LLMs, RAG, prompt engineering, tool calling, and agent workflows. Create APIs and integrations, monitor systems and costs, troubleshoot interconnected services, and collaborate with technical and business stakeholders on scalable solutions.

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.

Supports the development and maintenance of data pipelines, ETL/ELT processes, data infrastructure, integrations, and data quality checks. Assists with data lakes, warehouses, documentation, monitoring, and cross-functional delivery while participating in code reviews and learning activities. Requires SQL, basic programming knowledge, foundational data engineering concepts, and familiarity with a major cloud platform and data platforms such as Databricks or Snowflake.

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.

Develop and maintain data pipelines, ETL/ELT processes, data architectures, integrations, and data quality controls. Support data lakes, warehouses, APIs, governance, security, performance optimization, and technical documentation. Collaborate with senior engineers, analytics teams, data scientists, and business stakeholders to deliver reliable cloud-based data solutions. Use SQL and a programming language such as Python, Scala, or Java, while leveraging AI tools to improve engineering workflows.

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.

Build and maintain Azure Data Factory ingestion pipelines into OneLake, integrate ERP and CRM source systems, support Bronze-layer schemas and data contracts, ensure SLA compliance and pipeline reliability, troubleshoot incidents, and follow established architecture, platform standards, and CI/CD practices.

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.

Design, develop, and maintain scalable cloud data pipelines and infrastructure for analytics and machine learning solutions. Collaborate with cross-functional teams to ensure data integrity, usability, and quality. Build CI/CD pipelines for ETL processes, document workflows and architectures, and provide technical troubleshooting. The role requires Databricks, Spark, Python, SQL, dimensional modeling, and Data Lake or Data Warehouse experience, with Azure, AWS, Power BI, and Unity Catalog as beneficial skills.

Designs and develops large-scale data models, ETL pipelines, data lake and warehouse solutions, quality checks, monitoring, and data products. Partners with business, engineering, product, and third-party stakeholders to gather data requirements. Improves pipeline reliability and scalability, supports reporting and analytics, and helps onboard engineers. The role requires programming, distributed data processing, streaming, database, cloud, orchestration, and reporting-tool expertise.

Designs and develops large-scale data models, ETL pipelines, data lake and warehouse architectures, and data quality monitoring. Partners with business, engineering, product, and third-party teams to gather requirements and data. Manages data products, improves pipeline reliability and scalability, supports reporting and analytics, and helps onboard engineers. Requires strong programming, SQL, distributed computing, workflow orchestration, streaming, database, cloud, and communication skills.

Designs and builds scalable data models, ETL pipelines, data lake and warehouse architectures, data quality checks, monitoring routines, and data products. Collaborates with business, engineering, product, and third-party stakeholders to define and collect requirements. Maintains reliable data infrastructure, supports reporting and analytics use cases, and helps onboard new engineers.

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.

Build and maintain data platform tooling, shared libraries, data pipelines, automation workflows, and reusable lakehouse components. Develop observability integrations, CI/CD foundations, testing frameworks, schema and contract checks, and platform features that improve consistency and developer experience. Partner with data teams to support adoption through documentation, examples, and hands-on enablement. Work primarily with Python on Databricks and Azure Cloud infrastructure.

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.

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.

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.

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.

Build and maintain secure HR data pipelines, warehouse models, semantic layers, and AI-agent interfaces using Snowflake, dbt, Python, Airflow, AWS, and related technologies. Develop ETL infrastructure, data quality tests, monitoring, documentation, and optimized queries. Create semantic views and ontology layers for BI and LLM use cases, deploy Snowflake Cortex agents, and configure MCP connections for secure conversational access to certified HR data.