Top Data Engineer Jobs in Brasilia

Build machine learning systems that detect and de-identify sensitive information across text and image data. Develop data pipeline validation, adversarial test sets, regression gates, replacement pipelines, and audit processes. Measure precision, recall, leak rates, false negatives, and replacement consistency while investigating data quality issues across pipeline stages. The role requires strong Python, SQL, ML evaluation, privacy compliance knowledge, and experience with NER or similar detection systems.

Build and maintain Power BI dashboards, reports, semantic models, and data solutions using enterprise data from Snowflake and SAP HANA. Partner with business and technical stakeholders to define KPIs, gather requirements, integrate source data, troubleshoot issues across the data pipeline, and improve data quality, governance, consistency, and reporting reliability. Document analytics solutions and work collaboratively in an Agile environment.

Lead the replacement of a third-party digital ingestion tool with a custom ETL pipeline in AWS. Document legacy TapClicks logic, ingest data from Amazon and other DSP platforms, evaluate Fivetran versus custom solutions, and build a standalone pipeline supporting client dashboards. Collaborate with infrastructure and data science teams, align outputs with attribution models, and provide broader backend and application engineering support.

Build and operate a governed people-data platform supporting workforce analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, privacy controls, and reliability practices. Own the platform’s AI-readiness and infrastructure roadmap, monitor performance, manage incidents, and partner with People Analytics, HR Technology, Security, Legal, and Enterprise Data teams. Translate approved requirements into secure, production-ready technical solutions using Workday and Azure-based data technologies.

Build and maintain secure HR data pipelines, warehouses, and data marts using dbt, Python, Snowflake, BigQuery, Airflow, and AWS. Design semantic layers and ontology models for BI and AI agents, deploy Snowflake Cortex and MCP workflows, monitor pipeline reliability, enforce data quality and PII security, and document technical processes. Collaborate remotely with distributed teams aligned to EST.

Build and operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Designs and supports scalable AWS Glue and PySpark data ingestion pipelines from databases, files, APIs, and streaming sources into Amazon S3. Responsibilities include medallion architecture processing, data catalog management, quality controls, reconciliation, workflow orchestration, performance and cost optimization, production support, incident resolution, and governance collaboration.

Design, develop, integrate, deliver, maintain, and support data pipelines and applications. Administer PostgreSQL, Aurora, RDS, and SQL Server databases; build Databricks ingestion pipelines; architect scalable applications; troubleshoot production issues; and deliver customer-facing solutions using modern cloud, software engineering, Agile, and DevSecOps practices.

Build and maintain scalable ETL/ELT pipelines, real-time Kafka streaming applications, Snowflake data solutions, and cloud-native AWS architectures. Optimize high-volume data processing, ensure data quality and reliability, troubleshoot production pipelines, and collaborate with cross-functional teams on business-critical e-commerce data initiatives.

Transforms organizational data into actionable insights supporting revenue growth, operational efficiency, forecasting, and strategic planning. Responsibilities include building predictive models, developing scalable data tools, creating KPI dashboards and reports, leading end-to-end analytics projects, and presenting recommendations to senior leaders. The role partners with Finance and business stakeholders on planning and performance reviews while communicating complex findings through executive summaries and visual storytelling.

Lead enterprise data deployments across Latin America, integrating customer data into ClickHouse or warehouse-native Snowflake and BigQuery environments. Build orchestration pipelines, canonical data models, semantic layers, and AI-assisted discovery workflows. Partner directly with customer data teams, resolve complex integration challenges, optimize performance, and establish the region’s deployment playbook. Professional English and Spanish fluency are required, with regular customer travel and potential future relocation within Latin America.

Develop, maintain, and optimize financial data pipelines using Databricks, PySpark, Python, SQL, Delta Lake, and Azure. Support reconciliation, P&L routines, financial closing, data integration, quality, governance, auditability, and production troubleshooting. Process structured and semi-structured data, improve scalability and performance, and collaborate with finance, business, and technology teams. Contribute to integrating AI-driven capabilities into data workflows and document reliable, traceable solutions.

Build, maintain, monitor, and improve high-performance data pipelines across Azure and on-premises platforms. Design ETL/ELT processes, data lakes, data marts, and warehouses while ensuring performance, security, privacy, and reliability. Collaborate with BI, data science, and software engineering teams, contribute to data standards and code reviews, support incident resolution, and apply software engineering practices including Git and CI/CD. Mentor junior team members and integrate emerging data technologies.

Build secure, scalable data platform and governance automation capabilities. Develop controls for metadata, lineage, data quality, access, policy enforcement, identity, and auditability. Create secure APIs, MCP tools, enterprise integrations, automated testing and deployment, monitoring, and observability. Own production-quality code and documentation, support full lifecycle delivery, collaborate across technical and governance teams, and mentor engineers.

Design, build, deploy, and maintain AI-enabled applications for digital supply chain operations. Develop user interfaces, APIs, backend services, PostgreSQL databases, SQL queries, ETL/ELT pipelines, and integrations with BigQuery, SAP, and enterprise platforms. Implement LLMs, RAG, AI agents, and predictive models, while deploying containerized applications with Docker. Produce technical documentation and support production operations.

Lead the architecture and hands-on development of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, pipelines, orchestration, governance, observability, DataOps, CI/CD, performance, cost optimization, MLOps, and AI-readiness initiatives. Mentor analytical engineers, evaluate modern tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

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.

Own and develop relational database and lakehouse data layers for higher-education applications and analytics. Build optimized SQL schemas, procedures, and models; develop Databricks, PySpark, Spark SQL, and dbt pipelines; implement governance, security, quality checks, and lineage; manage CI/CD database changes; document systems, resolve incidents, collaborate with architects and developers, and mentor mid-level engineers.

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, builds, and maintains scalable data pipelines, data warehouse architecture, feature stores, model-training workflows, and real-time inference services. Develops semantic search, vector database, RAG, LLM orchestration, dashboarding, and AI-powered BI solutions. Partners with stakeholders, documents workflows, supports model deployment, and applies data engineering and MLOps best practices using Python, Spark, Hadoop, Kafka, SQL, and cloud databases.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Design, develop, and optimize enterprise data pipelines and lakehouse architecture using Databricks, Spark, and Delta Lake. Build ETL/ELT processes for banking data, improve cluster and query performance, establish data quality monitoring, and support regulatory compliance. Collaborate with technical and business stakeholders, document workflows, participate in code reviews, mentor junior engineers, and contribute to continuous improvement.

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.

Design, develop, and optimize scalable data pipelines using Databricks, Apache Spark, SQL, Python, Scala, and Azure. Process large datasets, improve pipeline performance and reliability, implement data quality and governance practices, troubleshoot complex issues, document solutions, and collaborate with technical teams, data professionals, business stakeholders, and clients.