Top Data Engineer Jobs in Brasilia

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.

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.

Design, build, test, and maintain cloud-based ETL/ELT pipelines for geospatial and operational data. Implement spatial transformations and geospatial data models in BigQuery, integrate APIs and feeds, enforce data-quality controls, troubleshoot discrepancies, and produce clear technical documentation while collaborating across distributed engineering teams.

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.

Develop and maintain SQL and dbt data transformations, Snowflake datasets, and analytical data models. Investigate data quality issues, optimize queries and workflows, support new data-source onboarding, and translate business requirements into reliable, documented datasets. Collaborate with developers, analysts, and stakeholders while supporting analytics, reporting, and dashboard solutions. Apply data modeling, testing, documentation, data lineage, and pipeline maintenance best practices.

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.

Design and maintain Azure-based data pipelines, model and integrate on-prem and cloud data, build Power BI/Tableau dashboards, write complex SQL/Kusto queries, manage cloud costs, and guide teams on data best practices within Agile teams.

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.

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.

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.

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 APIs and AI services, extend Python SDKs, and manage data quality through user acceptance testing. Collaborate with DevOps teams on CI/CD and infrastructure as code while working autonomously across cross-team dependencies.

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.

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, 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.

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.

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.

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