Top Data Engineer Jobs in Sao Paulo

Design, build, and deploy scalable data lakehouse and warehouse platforms; implement and optimize ETL/ELT pipelines (Databricks, dbt); develop data models and Medallion architecture layers; manage CI/CD and Infrastructure as Code (Azure DevOps, Terraform, CloudFormation); use cloud-native serverless offerings across major clouds; ensure data security and governance; integrate pipelines with BI tools like Tableau, Looker, and Power BI; communicate technical designs to stakeholders.

Design, build and own end-to-end data solutions and infrastructure. Implement scalable pipelines using cloud and big-data technologies, optimize performance, create REST APIs and CI/CD, coach teammates, and communicate technical solutions to business stakeholders.

Lead migration of tables, views, and stored procedures from Amazon Redshift to Databricks. Build and support Databricks pipelines/jobs, work with S3, Glue, and Athena within a modern data lake, and apply data governance (lineage, catalogs, RBAC). Collaborate with stakeholders during a 5–6 month engagement.

Support a 5–6 month migration from Amazon Redshift to Databricks: move tables, views, and stored procedures; build and maintain Databricks pipelines/jobs; use AWS services (S3, Glue, Athena); enforce data governance (lineage, catalogs, RBAC); work within a data lake architecture and collaborate with stakeholders. Requires 3–5 years data engineering experience and upper-intermediate English.

Design, deploy, and maintain ELT/ETL data pipelines on GCP using Airflow and dbt. Build scalable data platform components (Docker, CI/CD) and partner with stakeholders to deliver analytics and ML infrastructure. Apply software engineering best practices to ensure production-grade, high-quality data solutions.

Design, build, and scale GTM data pipelines, lakes, and analytics to support sales, marketing, and customer success. Ensure data integrity across GTM systems, implement batch and streaming ingestion, develop enrichment/LLM pipelines, create semantic models in dbt, and automate revenue workflows and ICP/territory scoring.

Build and maintain scalable batch and streaming data pipelines, develop and optimize data models in a cloud warehouse, implement ELT/ETL and orchestration (Airflow/Dagster), ensure data quality and monitoring, handle sensitive data with HIPAA-aligned practices, and evolve the platform's data architecture while partnering with analytics, product, and engineering teams.

As a Data Engineer at Tractian, you will manage data pipelines, automate workflows, and design a unified GTM data lake to support revenue analytics and improve processes.

The Data Engineer role involves developing and maintaining data pipelines, ensuring data quality, and collaborating with teams to deliver solutions tailored for financial services.

Design and maintain Azure-based data pipelines, model and integrate on-prem and cloud data, build Power BI/Tableau dashboards, write complex SQL/Kusto queries, manage cloud costs, and guide teams on data best practices within Agile teams.

Lead FinOps and data initiatives to optimize Databricks workloads, monitor cloud spend, build executive dashboards, define FinOps governance, and partner cross-functionally to drive cost-efficient architecture and measurable business outcomes.

Own image and video dataset lifecycle and quality, build and maintain data pipelines and versioning, create internal tools for labeling and curation, coordinate with labeling teams, and improve training-data collection using model-assisted and active-learning approaches.

Work with cross-functional teams to design, build, and deploy end-to-end data solutions. Own conception and implementation of data infrastructure, pipelines, ML model integration, REST APIs, and CI/CD. Collaborate, train, and present findings; stay current on cloud and big-data technologies.

Build, maintain, and monitor data pipelines and transformations using SQL, Python, and Spark. Troubleshoot pipeline failures and data quality issues, implement schema mappings and validation rules, support metadata, lineage, and data classification, automate operational tasks, contribute to testing and monitoring, and follow security, governance, and engineering best practices while collaborating with cross-functional teams.

As a Data Engineer, you'll design and maintain data models and pipelines, collaborate with teams, and ensure high-quality data for analytics.

Design, architect, and govern scalable data platforms (data lake/warehouse). Engage stakeholders to gather requirements, implement ETL pipelines, optimize performance, and manage data ingestion using Hadoop/Spark/Kafka/Airflow and AWS tools. Support data modeling, security, and workflow orchestration.

Responsible for managing data acquisition, curation, and publishing for analytics, developing data products, and creating scalable data pipelines.

Join the Data Foundry team as a Data Engineer, building pipelines and infrastructure for AI workflows, ensuring data quality and governance while maintaining and optimizing ETL processes.

Enable AI on a Data 4.0 platform using Snowflake Cortex: build AI-ready and semantic data products, support metric intelligence and anomaly detection, define reusable Cortex patterns, ensure governance, and collaborate with analytics and platform teams.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

Lead the design and implementation of robust data pipelines and ecosystems. Ensure scalability and reliability while mentoring team members in best practices.

As a Data Engineer, you'll maintain and enhance a data ingestion pipeline, collaborate with data scientists and engineers, and implement features for efficiency and scalability.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

The Senior Data Engineer will design, implement, and optimize data pipelines and processes, ensuring data access and quality across systems while collaborating with backend and analytics teams.