Top Data Engineer Jobs in Brazil

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Responsibilities include complex data transformations, performance and cloud cost optimization, troubleshooting pipeline bottlenecks, enforcing data quality and governance standards, and collaborating with technical and business teams to deliver production-grade data solutions.

Design, build, maintain, and optimize scalable ETL pipelines using PySpark and Databricks. Develop complex data transformations, improve performance and cloud cost efficiency, troubleshoot processing bottlenecks, and enforce data quality and governance standards. Collaborate with engineering and business teams to deliver production-grade data solutions and communicate technical concepts clearly.

Investigate and resolve enterprise data incidents using SQL and MS SQL Server; monitor Airflow, Azure Data Factory, and SQL Agent pipelines; troubleshoot Tableau dashboards and data warehouses; perform controlled data corrections; automate investigations with Python or PowerShell; and coordinate incident resolution through Jira Service Management with engineering, IT, and business teams.

Own and evolve databases, storage, networking, and Kubernetes infrastructure as platform products. Responsibilities include provisioning, backups, upgrades, capacity planning, performance tuning, migrations, incident leadership, root-cause analysis, on-call participation, and roadmap ownership. The role requires operating high-throughput ClickHouse or comparable OLAP systems, Ceph or S3-compatible storage, self-managed Kubernetes on bare metal, Linux performance debugging, and production networking expertise.

Build and optimize cloud-agnostic Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake. Integrate MLflow for AI model operationalization, implement CI/CD and Infrastructure-as-Code deployments, tune performance and cloud costs, and enforce data governance and security. Collaborate with architects, data scientists, engineering teams, and clients to deliver production-ready AI solutions and technical handoffs.

Own end-to-end data engineering for a Databricks-based commercial intelligence platform. Build entity resolution and reconciliation systems, evolve data models, onboard sources, and implement testing, monitoring, alerting, lineage, and incident analysis. Productionize data science models, analyze data quality and roadmap priorities, and provide technical leadership through code reviews. The role requires strong SQL, Python, PySpark, data modeling, orchestration, CI/CD, and hands-on Databricks experience.

Design, build, and optimize high-throughput batch and streaming data pipelines, cloud data warehouses, and lakehouse architectures for financial and transactional data. Implement data quality, testing, monitoring, DataOps, CI/CD, infrastructure as code, governance, privacy, and security practices. Collaborate with technical and product teams, ensure reliable and compliant production systems, and mentor junior and mid-level data engineers.

Design and build production AI applications, including advanced RAG pipelines, multi-agent workflows, and evaluation frameworks. Optimize system latency, throughput, scalability, resilience, and cost. Apply data science, machine learning, model monitoring, governance, and privacy practices. Provide technical leadership, mentor engineers, and collaborate with platform, data, AI, product, and consulting teams on robust AI-powered tools and integrations.

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 maintain curated data layers for a finance data platform using SQL and Snowflake. Develop dimensional models, business transformations, Slowly Changing Dimensions, reconciliation checks, data-quality controls, and temporal data logic. Support BI consumers through governed data objects and participate in code reviews, CI/CD promotion, and automated testing. Collaborate in English with distributed engineering teams and US stakeholders.

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.

Owns operational readiness for a production cloud data platform, including environment provisioning, CI/CD, multi-environment promotion, automated regression testing, monitoring, alerting, cost governance, runbooks, and client knowledge transfer. The role supports lakehouse, cloud warehouse, and CDC replication operations, with Snowflake preferred and Microsoft Fabric, OneLake, and Qlik Replicate as valuable experience.

Build and manage scalable open-source data infrastructure on Amazon EKS. Deploy technologies including Trino, Spark, Pinot, and Druid; implement infrastructure as code with Terraform and Helm; troubleshoot complex customer environments; and design novel, production-ready solutions using Kubernetes, AWS, and broad open-source technologies.

Develop Power BI dashboards, reports, semantic models, and reusable datasets that translate enterprise data into actionable insights. Partner with business and technical stakeholders to define KPIs, gather requirements, onboard Snowflake and SAP HANA data, troubleshoot end-to-end data flows, and improve data quality, governance, consistency, and reporting reliability in an Agile environment.

Design and maintain scalable data pipelines, ELT processes, and dbt transformations. Manage infrastructure with Terraform, contribute to Node.js and TypeScript web applications using GraphQL and React, and support monitoring, troubleshooting, code reviews, and production reliability. Collaborate with distributed data, product, and engineering teams while participating in a rotating on-call schedule.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Designs, develops, and maintains large-scale Databricks data pipelines using Python, SQL, PySpark, and Delta Lake. Integrates APIs, databases, and files; applies ETL/ELT, DataOps, CI/CD, testing, governance, and data quality practices. Collaborates with business, analytics, data science, and platform teams while mentoring peers and troubleshooting performance, scalability, and reliability issues.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.

Design, build, and scale reliable data infrastructure, including event ingestion, stream processing, data warehouses, ETL pipelines, analytics tools, and machine learning systems. Optimize performance, reliability, security, and data quality across AWS regions and data centers. Collaborate with analytics and data science teams, troubleshoot complex systems, influence architecture, conduct code reviews, and mentor data engineers while helping establish engineering best practices.

Lead the strategic roadmap and implementation of enterprise data platforms, including Lakehouse, ETL, streaming, and MDM capabilities. The role guides architecture, modernization, scalability, governance, reliability, data quality, and production readiness across multiple engineering teams. Responsibilities include mentoring engineers, defining standards, evaluating architectural trade-offs, driving platform optimization, and contributing hands-on to critical implementations in highly scalable, mission-critical environments.

Join a cross-functional team building and maintaining BEES data-driven features and tooling. Contribute to backend development, test automation, DevOps practices, quality strategy, and continuous improvement while partnering with product, design, and tech stakeholders.

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.

Designs and maintains scalable data pipelines, ETL/ELT processes, data integrations, semantic models, Power BI dashboards, and reports. Ensures data quality, governance, security, and performance while translating stakeholder requirements into actionable analytics solutions. Supports business users, troubleshoots reporting issues, provides training and documentation, and advances analytics capabilities through Power BI innovation.

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.