Top Data Engineer Jobs in Sao Paulo

Design, build, and maintain GTM data pipelines and data marts across Snowflake, Salesforce Data Cloud, and GCP. Model advertiser and revenue data, optimize SQL queries, support data quality and schema management, and collaborate with engineers and PMs to deliver GTM analytics and sales tooling.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

Design, deploy, and operate open-source data infrastructure (Trino, Spark, Pinot, Druid) on Amazon EKS. Build production-ready, scalable platforms using IaC (Terraform, Helm), containerization, and Kubernetes, while troubleshooting complex customer environments and delivering bespoke solutions.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

Design, build, and maintain scalable data pipelines, ETL processes, and data models; ensure data quality and consistency; work with cloud data platforms and BI tools to create dashboards and data products; collaborate with analysts, data scientists, and stakeholders to translate business needs into technical solutions.

Design, build, and optimize ETL/ELT pipelines and distributed data processing (Python/PySpark) on AWS. Implement data lake architectures, partitioning, and migrations from legacy RDBMS. Monitor pipeline health, ensure data quality and governance, support observability, and collaborate with analysts, scientists, and infra teams to deliver scalable analytics solutions.

Build and own end-to-end financial data pipelines and Snowflake/dbt architecture for the US revenue data chain. Deliver tested, documented ingestion and transformations, ensure pipeline reliability and on-call support through close, establish data quality and governance, partner with finance and analytics, and enable AI consumers of structured finance data.

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.

Build and maintain data enrichment pipelines, curate and label datasets, resolve data quality issues, prototype APIs/tools, collaborate with ML/product teams, and document reproducible workflows for large, messy industrial data.

Seeking a skilled Data Engineer with Apache Airflow experience to design, build, and maintain scalable data pipelines, optimize ETL processes, and provide technical leadership while collaborating with cross-functional teams.

Lead architecture and delivery of scalable data platforms and pipelines to support analytics, ML/AI products, reporting, integrations, and customer-facing data products. Productionize model outputs, enable LLM/VLM workflows, enforce data standards, ensure observability and data quality, and partner with Data Science to turn experiments into reliable production capabilities.

The Senior Data Engineer will modernize the client's data platform, focusing on pipeline migration and implementing a dbt-based transformation framework, while collaborating with engineering teams for knowledge transfer.

Lead technical direction and hands-on development of C360: design and build large-scale ETL pipelines, profile-matching/entity-resolution, ML and graph-based algorithms, scalable microservices/APIs, and AI-ready data services while mentoring engineers and shaping platform architecture.

Designs and delivers end-to-end data engineering solutions for enterprise clients, including production implementation, data integration, SQL optimization, cloud data platforms, automated transformation, orchestration, testing, deployment, and documentation. The role also requires client-facing communication, technical presentations, solution roadmaps, proof-of-concept development, and collaboration to ensure performance, security, scalability, and reliability.

Backend-focused full-stack engineer building scalable, data-driven applications, APIs, integrations, and data pipelines. Work includes cloud-hosted services, containerized deployments, frontend work with modern frameworks, and contributing to AI-powered features, architecture, and cross-team delivery.

Design, build, and operate analytics-ready data systems and production data pipelines. Integrate external APIs, run batch ELT into data warehouses, model data for reporting, implement orchestration (Airflow) and transformations (dbt), ensure monitoring and incident response, mentor engineers, and collaborate closely with analytics stakeholders to improve platform scalability, quality, and documentation.

Design, build, and maintain scalable ELT pipelines and modern data architectures. Develop and optimize transformations with dbt and SQL, implement infrastructure as code with Terraform, and support web integrations (Node.js, TypeScript, GraphQL, React). Improve data reliability and monitoring, participate in an on-call rotation, collaborate with US-based teams, and promote data engineering best practices.

Lead data engineering strategy and architecture, design scalable batch and streaming data platforms, drive AI-centric data initiatives, implement CI/CD and IaC, improve data quality/observability/governance, mentor engineers, and deliver large cross-functional production initiatives.

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.

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.

Design, build, and maintain scalable cloud data pipelines and warehouses (primarily Snowflake and BigQuery). Optimize ETL/ELT, enable analytics/BI and ML use cases, implement governance/security, collaborate with distributed engineering and analytics teams, document lineage and metadata, and monitor and troubleshoot pipeline reliability.

Build and operate scalable DataOps solutions across data engineering, cloud infrastructure, and DevOps. Develop Python, Bash, Terraform, and pipeline automation; orchestrate systems through APIs; deploy and maintain AWS infrastructure; support data modeling and documentation; establish DataOps best practices; and collaborate directly with customers to gather requirements and propose technical solutions.

Lead design and implementation of scalable data pipelines and cloud data warehousing solutions. Partner with stakeholders to translate requirements, build and optimize complex SQL, perform root-cause analysis, and deliver dashboards and reports for end-user analytics.

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.