Top Data Engineer Jobs in Brasilia

Designs, maintains, and monitors ETL pipelines across development, staging, and production environments. Builds data models, datasets, validation queries, DDL, and deployment scripts for institutional analytics. Customizes reusable pipelines for clients, troubleshoots production failures, supports platform modernization, and translates client requirements into SQL, configuration, and Airflow DAG changes while collaborating with analysts and stakeholders.

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.

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.

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.

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.

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.

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.

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.

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.

Own end-to-end data engineering projects and build scalable ELT pipelines using Snowflake, dbt, and Airflow. Responsibilities include performance tuning, dimensional modeling, data warehousing, BI data preparation, platform modernization, automation, risk identification, and mentoring engineers. The role also supports future AI/ML data capabilities and requires strong independent delivery in a post-acquisition SaaS data environment.

Develop and support enterprise Salesforce platforms through custom CRM development or Salesforce Data Cloud implementation. Responsibilities include building Apex, Lightning Web Components, Flows, integrations, automation, AI-enabled capabilities, data models, identity resolution, harmonization, SQL transformations, and scalable activation solutions. The role also involves security governance, performance optimization, troubleshooting, code reviews, release management, technical documentation, and collaboration with US-based teams during Central Time hours.

Build and maintain production data pipelines and reporting services for financial, payment, settlement, and transaction data. Extend merchant reports, contribute to a self-service Reporting API, manage concurrent priorities, participate in an approximately monthly on-call rotation, and support production incidents. The role emphasizes Python, SQL, AWS, Redshift, data accuracy, autonomous ownership, distributed collaboration, and AI-assisted development workflows, with Java backend development as a complementary skill.

Design, develop, and maintain scalable ETL/ELT pipelines and real-time streaming applications. Build and optimize data processing solutions with Python and Spark, implement Snowflake data warehousing, and architect cloud-native data services on AWS. Ensure data quality, performance, and reliability while troubleshooting production pipelines and collaborating with cross-functional teams.

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.

Design, build, and operate scalable data pipelines, models, and products supporting analytics, operational decisions, and AI. Own data architecture, dimensional modeling, data quality, lineage, orchestration, observability, and performance. Lead the migration from Redshift to Snowflake, establish engineering standards, and partner with technical and business teams to deliver trusted data solutions.