Top Data Engineer Jobs in Brasilia

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 cloud-native data pipelines, microservices, and AI-driven orchestration. Develop Python APIs (Flask/FastAPI), implement advanced SQL transformations (BigQuery), integrate LLMs/agents for automated workflows, and collaborate with Data Science and Product to deliver production-grade, observable, and secure AI-enabled data solutions.

The role involves leading the database and data warehousing infrastructure, optimizing performance, collaborating with development, and integrating new technologies.

The Principal Engineer for Data Infrastructure will manage database systems and data warehousing, optimizing performance and integrating new technologies in a fast-paced fintech environment.

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.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

Design, build, and optimize scalable ETL/ELT pipelines and modern Data Lake architectures on AWS. Develop PySpark and Python data workflows, optimize complex SQL, migrate legacy RDBMS workloads, implement partitioning strategies, monitor pipeline health, and support data governance, observability, and metadata management while collaborating with stakeholders and DevOps for IaC and resource provisioning.

Design, build, and operate scalable data pipelines and lakehouse solutions using Azure Databricks, DBT, Snowflake and Python. Lead migrations, implement data quality and observability, support production SLAs, and mentor junior engineers while ensuring governance, cost optimization, and reliability.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Lead and mentor a data engineering team while owning end-to-end data platform architecture and delivery. Drive migration from Snowflake/SSIS to Azure Databricks and DBT, implement Terraform IaC, CI/CD, and scalable ETL/ELT pipelines, ensure data quality, observability, and platform stability, and collaborate with stakeholders across product, data, and business teams.

Design, build, and maintain end-to-end scalable data pipelines and Snowflake-native warehouses; optimize SQL and performance; implement Azure-based data platform solutions; develop Power BI semantic models; enable AI/ML data workflows; enforce governance, CI/CD, and security; collaborate with stakeholders and mentor engineers.

Design and build scalable ETL/ELT data pipelines and a cloud-based data warehouse/lakehouse. Improve data quality, governance, and documentation. Work with stakeholders to enable analytics, BI, and machine learning by delivering clean, analytics-ready datasets and establishing data engineering best practices.

Design, build, and optimize SQL Server-based data layers and Azure data solutions. Own query performance, indexing, concurrency, CDC pipelines, ORM data access patterns (C#), and database CI/CD. Partner with engineers and architects, mentor team members, and ensure scalable, low-latency data platforms.

Own technical delivery and reliability of cloud-native data platform environments (AWS/Azure) for managed services clients. Build and maintain IaC and CI/CD pipelines, monitor and troubleshoot ETL/ELT jobs, provide technical leadership, mentor junior engineers, and contribute reusable templates, runbooks, and operational playbooks to standardize platform delivery.

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 maintain scalable ELT/ETL pipelines and data models for cloud cost attribution and financial reporting. Design a Cloud Economics data warehouse, perform end-to-end validation and auditing, automate cost-optimization recommendations, and deliver high-quality datasets for Product, Engineering, and Finance stakeholders.

As a Machine Learning Engineer/Data Scientist, you'll build and deploy ML solutions, collaborate with stakeholders, and analyze data, focusing on achieving measurable business impact.

Fusemachines is seeking experienced Senior Data Engineers to design and optimize scalable data pipelines and cloud-native architectures using modern technologies. This role targets candidates for future opportunities in data engineering, ensuring a quick evaluation process.

Lead architecture and implementation of scalable cloud-native data platforms and medallion-tiered pipelines. Optimize ETL/ELT, enforce data governance, security, and CI/CD/IaC. Mentor engineers, drive adoption of managed/serverless cloud services, integrate BI tools, and lead client-facing technical consulting and architectural decisions.

Join the data platform team to build and maintain cloud data infrastructure on GCP using Terraform. Ensure platform reliability, performance, monitoring, cost optimization, and collaborate with data engineers. Proactively identify improvements, security issues, and communicate status to technical and non-technical stakeholders in an international environment.

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.

Build scalable analytics applications and interactive visualizations. Develop backend data pipelines in Python and frontend interfaces in TypeScript. Transform business data into dashboards, improve data quality and performance, collaborate with product and design, and use AI-assisted tools to accelerate development. Hands-on, execution-focused role for remote work in Canada or LATAM.

Design and optimise Snowflake architectures and customer data foundations to support segmentation, personalisation, and audience activation. Build pipelines and reverse-ETL flows to Braze, Hightouch, and CDPs, implement identity resolution and consent frameworks, ensure data quality and governance, and collaborate with CRM, analytics, and marketing teams to enable multi-channel customer engagement and scalable customer 360 solutions.