Top Data Engineer Jobs in Sao Paulo

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy data warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production incidents. Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance while delivering well-tested Python and SQL solutions.

Build and operate batch and streaming pipelines on Databricks using PySpark and Delta Lake. Modernize legacy warehouses into governed Lakehouse architectures, optimize Spark jobs and Delta tables, implement data quality and governance controls, and troubleshoot production incidents. Collaborate with DevOps, platform, and analytics teams on security, observability, and compliance while writing tested Python and SQL. Use AI development tools to accelerate coding, testing, documentation, and prototyping.

Designs, maintains, and supports ETL pipelines and data workflows across development, staging, and production environments. Builds data models, datasets, validation queries, DDL, and deployment scripts for institutional analytics. Customizes reusable pipelines for clients, monitors production jobs, troubleshoots failures, supports modernization initiatives, and collaborates with analysts, client teams, and stakeholders to translate requirements into data solutions.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

Owns end-to-end data engineering for ingestion, medallion architecture, curated datasets, and Power BI semantic layers. Builds scalable ETL/ELT pipelines, integrates ERP and CRM systems, implements data quality and governance controls, optimizes performance and cloud costs, and provides technical leadership. Requires Microsoft Fabric or Azure expertise, Azure Data Factory, advanced Power BI and DAX skills, data contract design, and experience with Bronze/Silver/Gold architectures.

Designs, builds, and maintains production data pipelines and infrastructure supporting analytics, machine learning, and financial decision-making. Responsibilities include data ingestion, modeling, quality validation, workflow orchestration, database optimization, monitoring, troubleshooting, and managing scalable AWS or on-premises systems. The role collaborates with data scientists and analysts to ensure reliable access to large-scale and time-series financial data.

Designs and develops scalable data pipelines, automated ingestion processes, and data warehousing solutions. Builds dimensional and physical data models, transforms raw data using SQL and Python, and implements data quality validation and monitoring. Collaborates with analysts, data scientists, and business stakeholders to translate requirements into technical specifications. Applies data governance, access controls, security measures, and regulatory compliance practices.

Support Data & AI Governance initiatives through data analysis, curation, stewardship, documentation, cataloging, lineage, quality improvement, and policy maintenance. Use Python for analysis and automation while collaborating with technical and business stakeholders to improve data organization, accessibility, usability, and compliance with governance standards.

Design, develop, and maintain scalable cloud data pipelines and infrastructure for analytics and machine learning solutions. Collaborate with cross-functional teams to ensure data integrity, usability, and quality. Build CI/CD pipelines for ETL processes, document workflows and architectures, and provide technical troubleshooting. The role requires Databricks, Spark, Python, SQL, dimensional modeling, and Data Lake or Data Warehouse experience, with Azure, AWS, Power BI, and Unity Catalog as beneficial skills.

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.

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.

Build and operate reliable data infrastructure for scientific and operational use. Responsibilities include developing ingestion pipelines, orchestrated workflows, warehouse data models, integrations, testing, monitoring, alerting, documentation, and data quality investigations. The role collaborates with scientists and engineers to productionize datasets for geospatial and statistical analysis and improve data lineage, permissions, schemas, and self-service access.

Build and maintain data models, ETL/ELT pipelines, and reliable streaming integrations for high-volume telecom and customer data. Develop transformations in dbt, orchestrate workflows with Airflow, write optimized SQL, and implement data quality, monitoring, documentation, and reconciliation processes. The role partners with the Central Data team to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Lead the replacement of TapClicks with a custom ETL pipeline in AWS. Document legacy ingestion logic, evaluate Fivetran versus custom solutions, ingest Amazon DSP and other digital advertising data, and build a standalone system supporting client dashboards. Collaborate with data science teams to align outputs with attribution and scoring models, while also contributing to application and backend development.

Lead the replacement of TapClicks with a standalone ETL pipeline in AWS. Document legacy ingestion logic, evaluate Fivetran versus custom development, and ingest data from Amazon DSP and other programmatic advertising platforms. Design schemas and transformations, support backend application work, and coordinate with data science teams to align pipeline outputs with attribution, scoring, and dashboard requirements.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

Design and implement Salesforce Data Cloud solutions, including data ingestion, model mapping, identity resolution, harmonization, calculated insights, segmentation, and activation workflows. Build integrations using APIs, middleware, and ETL patterns; support data quality, governance, security, and compliance. Partner with business and architecture teams, troubleshoot platform issues, document technical designs, and maintain scalable customer data solutions.

Build and operate scalable batch and streaming data pipelines powering AI systems. Own ingestion, storage, transformation, serving, monitoring, validation, and continuous improvement across the data lifecycle. Partner with AI engineers and researchers to translate model requirements into reliable production infrastructure, using data lakes, warehouses, relational, graph, and vector databases. Define data strategy and ensure high-quality, observable, timely data for AI models and agents.

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 end-to-end data pipelines, models, data marts, and semantic layers for marketing, client, BI, product, and AI consumers. Integrate fragmented advertising and customer data across multiple platforms and tenants, manage dbt projects, ensure data quality, optimize performance, and support client-specific modeling. Use AI-assisted development workflows, collaborate cross-functionally, and deliver reliable, AI-ready data products.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Lead lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to onboard commercial, finance, R&D, and real-world data domains. Ensure data quality, observability, lineage, governed access, masking, and row- and column-level security while enabling reliable AI-agent access to enterprise data.

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

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.

Manage eDiscovery data-source insights, preservation operations, records, and metrics. Automate Microsoft Purview workflows using PowerShell and Microsoft Graph, build sync pipelines from Microsoft 365, Google Workspace, mobile devices, and endpoints into RelativityOne, and generate collection and processing reports. The role also supports process engineering, audit readiness, scalable data collection, and high-stakes digital forensics and litigation projects.