Top Data Engineer Jobs in Sao Paulo

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.

Design, build, and maintain a scalable open-source data lakehouse platform (S3, Apache Iceberg, ClickHouse). Develop and optimize ETL/ELT pipelines and orchestration (Temporal), ensure platform reliability and performance, collaborate with data scientists and engineers, and implement data quality, governance, and security best practices. Focus on batch processing and cloud-native microservices environments.

Lead data engineering architecture and standards, build and operate large-scale batch and streaming pipelines, partner with product and business teams to deliver scalable data solutions, manage technical debt, and balance build vs. buy decisions to maximize analytics and platform impact.

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

Build and operate the data platform behind regulatory indices: source messy public and commercial data, implement idempotent medallion ingestion pipelines, design Postgres schemas and migrations, construct auditable index methodology (normalization, weighting, sensitivity testing), and run pipelines on Prefect/ECS with strong observability and test coverage.

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

Design and maintain the graph data layer for a fashion advisory platform: schema design, batch ingestion, probabilistic entity matching and deduplication, editorial signal weighting, openCypher query optimization (sub-3s), and cross-functional collaboration with ML and GenAI agent developers.

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.

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.

Design, build, and maintain ELT/ETL pipelines and modular dbt models in Snowflake. Implement automated data quality tests, enforce Git/CI-CD workflows, monitor platform operations, resolve pipeline incidents, collaborate with BI and data science teams, and document data lineage and cataloging standards for a multi-country fintech analytics platform.

Design and build multi-modal data ingestion pipelines, manage embeddings in vector and graph databases, integrate AI-assisted agents, collaborate on evaluation pipelines, and deploy/optimize scalable data services and workloads on GCP using Python-based APIs and orchestration tools.

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.

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

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 Data Engineer will implement ETL/ELT solutions, design data pipelines, ensure data security, and optimize data processing systems. Responsibilities include data transformation and compliance collaboration.

As a Data Engineer, design and maintain data pipelines and infrastructure, collaborate with teams for efficient data delivery, and optimize data storage and retrieval processes.

The role involves understanding customer processes, validating technical designs of applications, estimating project efforts, supporting implementation activities, and mentoring colleagues in application architecture.

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.

Build and maintain ETL/ELT pipelines, model GTM data assets, optimize complex SQL queries, ensure data quality and governance across Snowflake/GCP, and collaborate with Data Engineers and PMs to support Salesforce-integrated revenue analytics and lead routing.

Lead architecture and implementation of scalable cloud-native data platforms and medallion-tiered pipelines. Optimize ETL/ELT, enforce data governance, security, and CI/CD/IaC. Mentor engineers, drive adoption of managed/serverless cloud services, integrate BI tools, and lead client-facing technical consulting and architectural decisions.

Architect and implement scalable ETL/ELT data pipelines, optimize Snowflake schemas, integrate APIs and event streams, maintain orchestration (Airflow/Prefect/Dagster), enforce data quality/lineage, and apply DataOps practices (CI/CD, Docker, IaC) with well-tested Python code to ensure reliable, production-grade data platforms.

Build and own end-to-end analytics data infrastructure: design and maintain ETL/ELT pipelines, model data in dbt, govern semantic layer and metrics, implement testing and observability, partner with stakeholders for requirements, run analyses and A/B test evaluation, and leverage AI tools to accelerate engineering and analytics workflows.

As a Senior Data Engineer, you will build and maintain data pipelines, collaborate with teams to convert requirements into data solutions, and optimize cloud-based data infrastructure.

Design, build, and maintain GTM data pipelines and marts across Snowflake, Salesforce Data Cloud, and GCP. Model advertiser and revenue data, write and optimize SQL/IQL, support ETL/ELT processes (dbt/Airflow), and ensure data quality and schema management for revenue analytics and sales tooling.