Top Data Engineer Jobs in Brazil

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.

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.

Develops and maintains data pipelines for ingestion, transformation, and processing. Builds data solutions using Python and SQL, works with diverse data sources, monitors and troubleshoots pipelines, and supports deployment through Git and CI/CD practices. Collaborates with engineers, analysts, and stakeholders to deliver reliable data solutions while improving data quality, performance, and scalability. Azure, Databricks, and PySpark experience are advantageous.

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.

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.

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.

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.

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.

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.

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 scalable Databricks-based data pipelines (PySpark, SQL) to process shipment, sell-out, and market share data. Collaborate with architects and product teams, reduce technical debt, provide L3 operational support, and deliver solutions within an Agile/Scrum environment.

Build and operate an enterprise data platform on Snowflake and Azure: design ETL pipelines, implement Bronze/Silver/Gold models with SQLMesh, provision infrastructure with Terraform, create CI/CD with GitHub Actions, enforce governance and HIPAA-aligned controls, implement observability, write production Python, and troubleshoot platform production issues.

Lead the design and ownership of ETL pipelines and analytical architecture for a high-volume, S3-backed OLAP data lake. Make decisions on partitioning, file formats, schema, and near-real-time processing; design DAG-based Airflow workflows; drive AWS data stack (Athena, EKS); review PRs; and lead senior engineers while working autonomously on underspecified problems.

Own ingestion, pipeline orchestration, and Single Source of Truth architecture for product modules. Build high-volume, containerized Python data pipelines on AWS, integrate external APIs and AI services such as AWS Bedrock, extend internal SDKs, and manage data processing through UAT. Collaborate with DevOps teams on CI/CD and infrastructure as code while independently ensuring data quality and handling edge cases.

Build and own high-volume data ingestion pipelines and Single Source of Truth architecture on AWS. Develop containerized Python workers on EKS, integrate external APIs and AI services including AWS Bedrock, extend internal SDKs, and manage data processing through UAT. Collaborate with DevOps teams on CI/CD and infrastructure as code while independently ensuring data quality and handling edge cases.

Build and own high-volume data ingestion pipelines and Single Source of Truth architecture on AWS. Develop containerized Python workers on EKS, integrate external APIs and AI services including AWS Bedrock, extend Python SDKs, and manage data quality through user acceptance testing. Collaborate with DevOps teams on CI/CD and infrastructure as code while independently handling edge cases and cross-team dependencies.

Build and own high-volume data ingestion pipelines and Single Source of Truth architecture on AWS. Develop containerized Python workers on EKS, integrate external data sources and AI APIs including AWS Bedrock, extend internal SDKs, and manage data-layer UAT. Collaborate with DevOps teams on CI/CD and infrastructure as code while independently identifying quality issues and edge cases.

Build and own high-volume data ingestion pipelines and Single Source of Truth architecture on AWS. Develop containerized Python workers on EKS, integrate external APIs and AI services including Bedrock, extend internal SDKs, and manage data quality through user acceptance testing. Collaborate with DevOps teams on CI/CD and infrastructure as code while independently handling edge cases and cross-team dependencies.

Build and own high-volume data ingestion pipelines and Single Source of Truth architecture on AWS. Develop containerized Python workers on EKS, integrate external APIs and AI services including AWS Bedrock, extend internal SDKs, and manage data-layer UAT. Collaborate with DevOps teams on CI/CD and infrastructure as code while independently ensuring pipeline quality and handling edge cases.

Manage and optimize PostgreSQL, SQL Server, and Oracle databases for performance, security, availability, and scalability. Design schemas, indexes, and partitions; oversee monitoring, backups, recovery, disaster planning, and compliance. Translate business needs into data engineering work, support Power BI solutions, lead standups, prioritize team workloads, mentor colleagues, and communicate with stakeholders. Apply AI and automation to database tuning, reliability validation, migration planning, testing, and operational efficiency.

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.

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.