Senior Data Engineer GCP in Brazil, Indiana at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer GCP based in Brazil.

This is a high-impact engineering role within a large-scale, cloud-native data platform environment powering real-time and batch data processing for a digital streaming ecosystem. You will contribute to the design and evolution of highly scalable, event-driven systems that handle massive volumes of user behaviour and operational data. The role blends data engineering, software development, and cloud platform ownership, with a strong emphasis on reliability, performance, and observability. You will work in a collaborative, autonomous engineering culture where cross-functional teams build and operate mission-critical data services. The environment is fast-paced and technically complex, requiring strong systems thinking and a passion for distributed architectures. This is an opportunity to help shape a modern data platform built entirely on Google Cloud technologies.

• Design, build, and maintain scalable data processing platforms and services on Google Cloud Platform (GCP), ensuring performance, reliability, and scalability at scale.
• Develop event-driven architectures using native GCP services such as Pub/Sub, Dataflow, BigQuery, and Cloud Functions.
• Build and maintain data pipelines and backend services using Python, supporting both batch and real-time data processing needs.
• Contribute to infrastructure automation and cloud provisioning using Terraform, ensuring repeatable and scalable deployments.
• Support and optimize containerized workloads running on Google Kubernetes Engine (GKE), ensuring stability and performance.
• Implement monitoring, logging, and observability solutions to ensure operational excellence across data services and pipelines.
• Collaborate closely with product, data, and engineering teams to deliver reliable platform capabilities and improve end-to-end data workflows.
• Participate in platform operations, including incident support, troubleshooting, and continuous improvement initiatives.
• Contribute to CI/CD improvements, testing strategies, and software delivery best practices.
• Take part in architectural discussions and contribute to the long-term evolution of the data platform.

• Strong experience in cloud-native engineering environments with a focus on data platforms and distributed systems.
• Hands-on expertise with Google Cloud Platform (GCP), including services such as BigQuery, Pub/Sub, Dataflow, and Cloud Functions.
• Strong programming experience in Python, including building production-grade data pipelines and services.
• Solid understanding of distributed systems architecture and event-driven design patterns.
• Experience with Infrastructure as Code using Terraform in production environments.
• Familiarity with containerized applications and Kubernetes concepts (GKE experience is a plus).
• Experience building and maintaining CI/CD pipelines and working with modern DevOps practices.
• Strong analytical and problem-solving skills with a systems-thinking mindset.
• Experience working in Agile environments, ideally using Scrum methodologies.
• Excellent English communication skills, both written and verbal.
• Exposure to Kafka, Go, Java, or advanced observability tools (Grafana, Prometheus) is considered an advantage.

• Opportunity to work on a large-scale, cloud-native streaming data platform
• Fully remote or hybrid flexibility depending on location (Brazil/Portugal only)
• Collaborative and autonomous engineering culture with shared ownership
• Strong focus on learning, mentoring, and continuous technical development
• Exposure to modern GCP-based architecture and distributed systems at scale
• Agile environment where experimentation and innovation are encouraged
• Opportunities to contribute to high-impact architectural decisions
• Potential for international collaboration and travel opportunities
• Culture that values learning from failure and continuous improvement