Sr. Software Development Engineer
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 8+ years of software engineering experience
- Required Skills
- AWSDockerGraphQLKubernetesTypeScriptC#.NETReactCI/CDRESTful APIsTerraformGitLabUnit TestingDistributed Systems
Requirements
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
- 8+ years of software engineering experience in building and operating complex, scalable systems.
- Strong hands-on experience with .NET (C#) for backend services and React (TypeScript) for frontend development.
- Proven experience designing distributed systems and building RESTful and/or GraphQL APIs in production environments.
- Solid experience with AWS and cloud-native architectures.
- Experience with Infrastructure as Code tools such as Terraform.
- Strong knowledge of CI/CD pipelines (e.g., GitLab), Docker, and Kubernetes in production environments.
- Experience working with relational and/or NoSQL databases at scale.
- Strong testing mindset with experience in unit, integration, and end-to-end testing strategies.
- Demonstrated experience using AI tools to improve engineering outcomes such as productivity, quality, or automation.
- Strong communication skills in English, with ability to collaborate effectively in global teams.
- Strong ownership mindset, mentoring ability, and experience leading technical discussions.
Responsibilities
- Design, develop, and maintain scalable backend and frontend services using .NET (C#) and React (TypeScript), ensuring performance, reliability, and maintainability.
- Build and evolve distributed systems and APIs (REST and/or GraphQL) with clear contracts, observability, and defined SLIs/SLOs.
- Embed AI-assisted development into daily workflows to improve coding speed, testing quality, documentation, and delivery efficiency.
- Contribute to architecture and technical design decisions for complex systems, balancing scalability, cost, and customer impact.
- Improve CI/CD pipelines (e.g., GitLab), increasing automation, deployment reliability, and engineering efficiency.
- Strengthen system reliability through improved monitoring, alerting, incident response, and post-incident learning practices.
- Mentor engineers through code reviews, pairing, and technical guidance, raising overall team engineering standards.
- Collaborate with global cross-functional teams to align on requirements, technical tradeoffs, and delivery outcomes.
- Develop AI-enabled capabilities that enhance automation, data processing, and customer-facing insights.
- Evaluate and improve AI-generated outputs, ensuring security, governance, and engineering quality standards are met.
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