Design, develop, and maintain large-scale data pipelines using Scala, Spark, and cloud-native technologies
Solve complex data engineering challenges by designing efficient, resilient distributed systems
Contribute to production-grade data platforms, delivering high-quality, tested code
Ensure software quality through clean, testable, and well-documented code
Collaborate within agile value stream teams, working closely with engineers, product owners, and cross-functional stakeholders
Provide technical guidance to peers, contributing to architecture discussions and promoting best engineering practices
Support release and operational processes, including deployments, monitoring, alerting, and continuous improvement of system reliability
Drive improvements in performance, cost efficiency, and scalability of data processing workloads

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
Solid experience in software or data engineering
Hands-on experience with cloud-native data platforms (AWS, GCP, or Azure), including services such as S3, Glue, Athena, or equivalent
Strong understanding of distributed systems, resiliency patterns, data partitioning, and efficient data access strategies
Experience designing and operating highly parallelized data workloads
Proficiency with Git-based version control systems (GitHub, Bitbucket, etc.)
Familiarity with agile methodologies and collaborative development in multi-team environments
Strong communication skills, with the ability to contribute in technical discussions and support peer development
Advanced English proficiency and availability to travel to São Carlos/SP when needed

Mid Software Engineer

Key skills