Top Data Engineer Jobs in Sao Paulo

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

As an ETL Data Engineer, you'll design and maintain data integration processes using Python and Azure Synapse Analytics, collaborating across teams to ensure data quality and performance optimization.

The role involves developing, maintaining ETL data processes using Python and Azure Synapse Analytics, designing data storage structures, collaborating with teams for data models, and ensuring data quality and compliance.

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.

We are looking for a Staff Software Engineer for our MLS data platform to develop data pipelines, backend services, and AI-driven features for the real estate industry.

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.

As a Sr. Data Engineer, you'll build and scale high-throughput streaming pipelines, model real estate datasets, and improve data quality using AI-driven tools in a fast-paced environment.

Design, build, and maintain cloud-native data pipelines ingesting industrial and historian data into GCP. Process large Parquet datasets, optimize BigQuery/Bigtable storage, support ML teams with production-ready datasets, integrate OT systems, troubleshoot data quality/performance, and operationalize analytics back into control environments.

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 pipelines and infrastructure to ingest, clean, validate, and deliver training-ready datasets for ML. Integrate ETL/ELT processes, enforce data quality and governance, support labeling pipelines, and collaborate on architecture, code reviews, and documentation.

As an ETL Data Engineer, you will develop and maintain ETL processes, optimize data storage, ensure data quality, and collaborate with stakeholders.

As an ETL Data Engineer, you will develop and maintain ETL processes using Python and Azure Synapse Analytics and collaborate with data scientists and analysts.

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.

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.

Design, build, optimize, and monitor scalable data pipelines and infrastructure using Python, PySpark, Airflow, AWS Glue, Snowflake, and PostgreSQL. Implement DAGs, observability, automation, and reusable connectors while collaborating with platform and AI/ML teams to enable ingestion, validation, and real-time workflows.

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.

Design and operate scalable, low-latency ETL/ELT and streaming data pipelines across ClickHouse, Postgres, Athena, and cloud sources; manage schema evolution, data modeling, sharding/partitioning, CI/CD for data, monitoring, and lineage; collaborate with engineers and data scientists to productionize ML/AI models and mentor team members.

Design, build, and maintain scalable cloud-native data pipelines and ELT/ETL workflows in AWS. Develop and optimize Snowflake data models, ingest structured and unstructured data, and use Databricks/Spark to process large datasets. Implement data quality, governance, monitoring, and orchestration. Collaborate with data scientists, analysts, and engineers to support analytics, reporting, and AI/ML initiatives while optimizing performance and cost.

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.

Design and build scalable Snowflake-based data models and pipelines using SQL and Python. Integrate data via APIs, optimize performance, and collaborate cross-functionally to support reporting and analytics while improving data engineering standards.

Design, build, and maintain scalable ETL/ELT data pipelines using dbt, Alteryx and Python. Monitor and troubleshoot production RunOps, ensure data quality, and create MicroStrategy dashboards and reports to meet business requirements.