Accountabilities:

Design, develop, and maintain large-scale data pipelines using Apache Spark and cloud-native technologies, ensuring performance, scalability, and reliability.

Solve complex data engineering challenges by building efficient distributed systems and optimizing data processing workflows.

Contribute to production-grade data platforms through high-quality, tested code and end-to-end feature development.

Maintain clean, reusable, testable, and well-documented code while following engineering standards and participating in code reviews.

Collaborate within agile value stream teams, working closely with engineers, product owners, and cross-functional stakeholders.

Contribute to technical discussions, architecture decisions, and peer development by sharing knowledge and promoting engineering best practices.

Support software releases and operational processes, including deployments, monitoring, alerting, and continuous reliability improvements.

Identify and implement opportunities to improve processing performance, cost efficiency, and platform scalability.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Solid professional experience in software engineering or data engineering.

Experience designing and operating highly parallelized data workloads.

Proficiency with Git-based version control systems such as GitHub or Bitbucket.

Familiarity with agile methodologies and collaborative development across multiple teams.

Strong communication skills and the ability to contribute effectively to technical discussions and support colleagues.

Advanced English proficiency.

Availability to travel to São Carlos, São Paulo, when required.

Nice to have:

Hands-on experience with cloud-native data platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.

Familiarity with cloud services such as Amazon S3, AWS Glue, and Amazon Athena, or equivalent technologies.

Experience building and optimizing large-scale data pipelines using Scala and Apache Spark.

Knowledge of functional programming, particularly Scala, or experience with languages such as Java, Python, or Rust.

Familiarity with modern data platform architectures and large-scale data ecosystems.

Benefits

Opportunity to work on large-scale, cloud-native data platforms and business-critical technology solutions.

Collaborative environment with agile teams and global stakeholders.

Opportunities to contribute to technical architecture, engineering standards, and continuous improvement initiatives.

Inclusive workplace culture focused on diversity, belonging, and professional development.

Access to employee affinity groups and initiatives supporting inclusion, representation, and equal opportunity.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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