Top Data Engineer Jobs in Brasilia

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.

Build and evolve a SaaS platform for lab testing and regulatory compliance. Responsibilities include full-stack development, systems architecture, technical trade-off evaluation, security implementation, and close collaboration with founders and customers during early-stage product development.

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.

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.

Designs, develops, and maintains scalable cloud data pipelines and infrastructure for analytics and machine learning. Responsibilities include ETL CI/CD, data quality, dimensional modeling, data lake and warehouse implementation, governance, workflow documentation, and technical troubleshooting. The role requires Databricks, Spark, Python, SQL, strong English communication, and experience with modern cloud data engineering stacks.

Build production LLM agents, multi-step workflows, MCP servers, evaluation and observability systems, and document-extraction pipelines. Architect AI-ready semantic data layers over Snowflake and dbt, develop Python ingestion and Airflow workloads, and implement retrieval, embeddings, governance, security, and reliable agent operations. Lead technical design, mentor peers, and advise clients in an embedded consulting engagement.

Lead a data engineering squad delivering strategic, low-latency data products on Azure. Responsibilities include designing data architectures, building Python REST APIs and Databricks/dbt pipelines, implementing Terraform infrastructure, ensuring data quality and observability, driving CI/CD, resolving production issues, and mentoring engineers. The role requires hands-on technical leadership, advanced data modeling and SQL expertise, and collaboration with business and technical stakeholders.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

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.

Lead end-to-end development of data products: ingest, model, transform, validate, and deliver datasets for analytics and AI. Translate business requirements, ensure data quality, build scalable pipelines (SQL/Python/BigQuery), enable AI consumption, and communicate solutions to stakeholders while driving proactive improvements.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

Own the end-to-end production data platform supporting AI agents. Build and operate API, object-storage, and event-stream ingestion pipelines; model governed Delta Lake layers in Databricks and Unity Catalog; develop low-latency serving layers; and implement data quality, monitoring, observability, CI/CD, and Terraform-based infrastructure. Optimize performance and cost, collaborate with AI, software, and product teams, and use coding agents responsibly. This Brazil-based contract requires strong Python, advanced SQL, Azure, and production data engineering expertise.

Design and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines across AWS, Azure, and GCP. Build Spark and Delta Lake workflows for structured and unstructured data, tune performance and cloud costs, and integrate MLflow for AI model operationalization. Implement CI/CD and infrastructure-as-code deployments, enforce governance and security through Unity Catalog, and collaborate with client leaders to deliver production-ready data and AI solutions.

Develop and maintain data pipelines for collection, processing, cleaning, and real-time stream processing. Collaborate with stakeholders to define data requirements, improve data quality and security, and manage cloud data infrastructure using AEP, Google Cloud, AWS, Snowflake, and Airflow. Apply Python and AI expertise to enhance data processing and analysis capabilities while addressing data-related challenges and emerging technology trends.

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.

Build and operate Kubernetes-based data security services across public cloud, private cloud, and on-premises environments. Develop deployment, infrastructure, configuration, observability, and operational automation; troubleshoot complex production issues across applications, networking, and cloud platforms; improve scalability and reliability; support on-call operations; and contribute to technical initiatives, tooling, runbooks, and engineering best practices.

Develop and maintain cloud-based data pipelines supporting a centralized data lake and mobile field application. Extend ETL workflows for regional expansion, troubleshoot production issues, transform data with Python and SQL, manage data warehouses, and enforce data quality. Collaborate with software and architecture teams on data modeling, contribute to data architecture, and document pipelines and integrations.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

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.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.