Top Data Engineer Jobs in Sao Paulo

Designs and optimizes scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Builds agentic workflows for data validation, troubleshooting, and operational automation. Develops reusable data engineering frameworks, implements quality controls and monitoring, optimizes workloads, and supports testing, deployment, productionization, and ongoing enhancement of reliable data solutions.

Develop and optimize scalable ETL/ELT pipelines using Databricks, PySpark, SQL, GCP, BigQuery, and Delta Lake. Build agentic workflows for data validation, troubleshooting, and operational automation. Implement data quality controls, monitoring, exception handling, testing, deployment, and production support while optimizing performance and reliability. Collaborate with business, data engineering, and platform teams to create reusable frameworks and production-grade data solutions.

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.

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 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.

Drive technical sales and customer adoption of Microsoft's cloud database and analytics platform through demos, proof-of-concepts, hackathons, and architecture workshops. Design secure, scalable solutions, guide migrations, resolve technical blockers, and collaborate with engineering teams. Maintain expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and BI solutions while building trusted relationships with enterprise platform leaders.

Build and maintain data pipelines, transformation models, and analytical datasets for media and entertainment clients. Translate complex business requirements into dbt and SQL models, aggregate data from platforms such as Tubi, Roku, YouTube, and Amazon, and integrate internal contract data. Use Snowflake, Python, GitHub, and AI development tools while improving documentation, governance, data quality, and collaboration with product managers and analytics stakeholders.

Build and maintain data pipelines, transformation models, and analytical datasets for media and entertainment clients. Translate business requirements into dbt and SQL models, integrate revenue and performance data from major platforms, and use Snowflake, Python, and GitHub-based tools. Collaborate with product managers, visualization leads, analysts, and technical leadership to deliver reliable, consumption-ready data and improve AI-assisted development practices.

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, 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.