Top Data Engineer Jobs in Sao Paulo

Build, deploy, and maintain production-grade ML models and data pipelines in cloud environments. Perform EDA and feature engineering, monitor model performance and drift, and collaborate with engineering and business stakeholders. Contribute to AI/LLM initiatives and ensure solutions operate reliably in production.

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.

As a Software Engineer for Data Infrastructure at Speechify, you will manage data collection, enhance cloud infrastructure, and collaborate with researchers to optimize datasets for AI models.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Senior UI developer responsible for designing and building responsive React-based UIs and dashboards for cybersecurity analytics. Develop backend services and APIs in Python, integrate security data sources, optimize SQL Server/PostgreSQL reporting, collaborate with stakeholders, influence architecture, and mentor junior developers.

Design, build, and maintain reliable, scalable ELT/ETL data pipelines and expand the data warehouse. Partner with technical and non-technical stakeholders to deliver analytics-ready data, support reporting, and work with cloud-based data stack technologies.

As a Senior Data Engineer at Zencore, you will leverage Google Cloud to assist clients in data transformation and application modernization, covering ETL/ELT, data warehousing, and streaming data systems.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Validate enterprise ETL/ELT pipelines, database objects, and BI reports to ensure data accuracy and integrity. Build automated tests, write complex SQL, analyze query performance, and track defects using JIRA/TestRail while collaborating with developers and DBAs to improve data quality.

Build and maintain scalable, high-performance data pipelines and platforms using Spark/Scala/Python. Own end-to-end projects, collaborate with product and global teams, mentor junior engineers, implement testing and performance tuning, and contribute to the technical roadmap.

Own and deliver end-to-end data engineering projects: design, build, test, deploy, and maintain scalable big-data pipelines and APIs. Collaborate with product and global teams, mentor junior engineers, monitor performance, and drive technical roadmap and stability improvements.

Design, build, and optimize semantic Tableau data models, dashboards, reports, and datasets. Integrate and validate Databricks data pipelines, maintain data ingestion from APIs and databases, lead Looker-to-Databricks+Tableau migration, ensure data governance, performance, and security, and collaborate with stakeholders to translate business needs into scalable BI solutions.

Design, build, and maintain scalable, incremental Databricks/Spark data pipelines and dimensional models. Optimize large-scale Spark workloads, ensure data quality and reconciliation, produce documentation, and collaborate with stakeholders to deliver trusted analytical datasets and metrics.

Operate and support ELT pipelines, design and maintain data warehouse and semantic-layer models, create BI data models and leadership reporting, troubleshoot data issues, prepare data for AI workflows, and collaborate with stakeholders to improve analytics infrastructure and recurring reporting processes.

Design and build scalable data pipelines and distributed processing systems on Databricks. Lead technical design, develop advanced data models, champion CI/CD and IaC, ensure operational reliability, mentor engineers, and collaborate cross-functionally to support analytics and ML use cases.

Design, build, and maintain scalable ETL/ELT pipelines and cloud-native Data Lake/Warehouse solutions on Azure. Implement data integration, quality, governance, dimensional models (SCD1/2), Python-based automations, CI/CD, and monitoring. Collaborate with international stakeholders to optimize performance, enable analytics, and support FinOps and deployment for enterprise data platforms.

The Data Scientist will lead data-driven decision-making, develop machine learning models, design automated pipelines, and improve product performance.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Design, build, and maintain scalable Airflow-based data pipelines and reusable components. Implement CI/CD for deployments, enforce data governance and quality, monitor and troubleshoot workflows, document solutions, and mentor teammates on Airflow best practices.

Deploy and maintain Shift's fraud detection solutions for a pod of clients: manage ETL and data pipelines, develop/test/tune/deploy models, roll out platform features, optimize production environments, engage stakeholders, report performance, and mentor junior data scientists.

Lead design, build, and optimize a multi-country Snowflake data platform on GCP. Build CDC ingestion pipelines, implement dbt semantic models, manage Snowflake deployments via DCM/GitHub CI/CD, enforce governance/RBAC/PII protections, optimize FinOps/performance, and mentor data engineers while aligning stakeholders across Product, Risk, BI, and regional teams.

Design, build, and maintain cloud data pipelines and Snowflake/dbt data models; develop AI-powered applications using LLMs, RAG, tool-calling, and agent workflows; create APIs and integrations; monitor performance, data quality, and costs; troubleshoot interconnected systems; apply security, governance, testing, and CI/CD best practices; partner with stakeholders to deliver scalable solutions.

Own and scale the enterprise LookML semantic layer to deliver performant, reusable Looker data products. Translate stakeholder needs into durable models, optimize query performance, govern LookML across instances, mentor junior staff, and support conversational analytics and AI-driven queries.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Lead and mentor a data engineering team while owning end-to-end data platform architecture and delivery. Drive migration from Snowflake/SSIS to Azure Databricks and DBT, implement Terraform IaC, CI/CD, and scalable ETL/ELT pipelines, ensure data quality, observability, and platform stability, and collaborate with stakeholders across product, data, and business teams.