Mid Software Engineer
RemoteBrazil
BrazilRemoteFull TimeMid LevelBachelors DegreeEnterprise
Full TimeMid LevelBachelors DegreeEnterprise
Job Summary
Design and maintain large-scale data pipelines using Scala, Spark, and cloud-native technologies to optimize distributed processing systems and ensure reliability of petabyte-scale workloads. Solve complex data engineering challenges by building efficient, resilient systems and contributing to production-grade platforms with clean, testable code. Collaborate within agile value stream teams to deliver business-critical solutions, providing technical guidance on architecture and code reviews while supporting release processes, monitoring, and continuous improvement of system performance and cost efficiency.
Required Qualifications
- 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
- Availability to travel to São Carlos/SP when needed
Desired Qualifications
- Experience with DBT or other data transformation frameworks
- Knowledge of concurrent and parallel programming patterns
- Expertise in building and optimizing large-scale data pipelines using Scala and Apache Spark
- Experience with functional programming (Scala) or languages such as Java, Python, or Rust
- Familiarity with modern data platform architectures and large-scale data ecosystems
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