Mid Data & Software Engineer

Jobgether; Brasil; Presencial; BRL 150 000 - 230 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Global collaboration

Professional development

Travel opportunities

Diversity and inclusion

Resumo da oferta

Jobgether is seeking an experienced Data Platform Engineer to design, build, and evolve scalable data pipelines in cloud-native environments. You will work with Scala, Apache Spark, and distributed systems to support high-volume workloads, ensuring performance, reliability, and operational excellence.

You will collaborate with agile teams, stakeholders across regions, and contribute to architecture discussions, code reviews, and production readiness.

Qualificações

• Bachelor's degree in Computer Science, Engineering, or related technical field or equivalent practical experience.
• Professional experience in software or data engineering.
• Hands-on experience with cloud-native data platforms such as AWS, GCP, or Azure.
• Familiarity with cloud data services such as S3, Glue, Athena, or equivalent.
• Strong understanding of distributed systems, resiliency patterns, and data access strategies.
• Experience designing, developing, or operating highly parallelized data-processing workloads.
• Knowledge of software engineering principles, testing, and version control.
• Advanced English proficiency for collaboration with international stakeholders.

Responsabilidades

• Design, develop, and maintain large-scale data pipelines using Scala, Spark, and cloud-native platforms.
• Build scalable and resilient data processing solutions for large workloads.
• Solve data engineering challenges with efficient distributed systems and optimized workflows.
• Deliver clean, tested, maintainable, and well-documented code for production use.
• Participate in end-to-end feature development from design to deployment and support.
• Apply best practices through code reviews, testing, and documentation.
• Collaborate in agile value stream teams with engineers, product owners, and stakeholders.
• Contribute to architecture discussions and continuous improvement of engineering practices.
• Provide technical guidance and support to peers to promote quality engineering standards.
• Support release and operational activities including monitoring, alerts, and reliability improvements.
• Identify opportunities to improve performance, scalability, and cost efficiency in data processing.

Conhecimentos

Distributed systems

Cloud computing

Team collaboration

Agile methodologies

English proficiency

Formação académica

Bachelor's degree in Computer Science or related field

Ferramentas

Scala

Apache Spark

AWS

GCP

Azure

S3

Glue

Athena

Git (GitHub/Bitbucket)

DBT

Descrição da oferta de emprego

This role offers the opportunity to design, build, and evolve highly scalable data platform solutions that support critical business products. You will work with large-scale, cloud-native data pipelines and distributed processing systems, helping ensure performance, reliability, and resilience across demanding workloads. The position combines software engineering and data engineering, with a strong focus on scalable architecture and production-grade solutions. You will collaborate within agile, cross-functional teams and work with stakeholders across different regions to deliver impactful data capabilities. Beyond development, you will contribute to system optimization, operational excellence, technical discussions, and continuous improvement. It is an environment suited to engineers who enjoy solving complex data challenges and working with modern cloud and distributed technologies.

Accountabilities:

• Design, develop, and maintain large-scale data pipelines using technologies such as Scala, Apache Spark, and cloud-native platforms.
• Build highly scalable and resilient data processing solutions capable of supporting large-volume and complex workloads.
• Solve challenging data engineering problems by designing efficient distributed systems and optimizing data processing workflows.
• Contribute to production-grade data platforms by delivering clean, tested, maintainable, and well-documented code.
• Participate in end-to-end feature development, from technical design and implementation through deployment and operational support.
• Apply engineering best practices through code reviews, automated testing, documentation, and adherence to established development standards.
• Collaborate within agile value stream teams, working closely with engineers, product owners, and cross-functional stakeholders to deliver effective technical solutions.
• Contribute to architecture discussions, technical decision-making, and the continuous improvement of engineering practices.
• Provide technical guidance and support to peers, promoting knowledge sharing and strong software engineering standards.
• Support release and operational activities, including deployments, monitoring, alerting, troubleshooting, and reliability improvements.
• Identify and implement opportunities to improve data-processing performance, scalability, reliability, and cost efficiency.
• Help ensure the availability and operational effectiveness of distributed data workloads in production environments.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• Professional experience in software engineering, data engineering, or a closely related technical discipline.
• Hands-on experience working with cloud-native data platforms such as AWS, GCP, or Azure.
• Familiarity with cloud data services such as S3, Glue, Athena, or equivalent technologies.
• Strong understanding of distributed systems, resiliency patterns, data partitioning, and efficient data access strategies.
• Experience designing, developing, or operating highly parallelized data-processing workloads.
• Solid understanding of software engineering principles, including clean code, testing, version control, and maintainable system design.
• Proficiency with Git-based version control platforms such as GitHub or Bitbucket.
• Familiarity with agile methodologies and collaborative development within multi-team environments.
• Strong communication and collaboration skills, with the ability to participate effectively in technical discussions and support peer development.
• Advanced English proficiency, with the ability to collaborate with international stakeholders.
• Availability to travel to São Carlos, São Paulo, when required.
• Experience with DBT or other data transformation frameworks is a plus.
• Knowledge of concurrent and parallel programming patterns is a plus.
• Hands-on expertise with Scala and Apache Spark for large-scale data pipelines is a plus.
• Experience with functional programming in Scala or with languages such as Java, Python, or Rust is a plus.
• Familiarity with modern data platform architectures and large-scale data ecosystems is an advantage.

Benefits

• Opportunity to work on large-scale, cloud-native data platforms and distributed systems.
• Exposure to modern technologies including Scala, Apache Spark, cloud services, and large-scale data processing.
• Opportunity to contribute to solutions handling highly complex and high-volume workloads.
• Collaboration with global engineering, product, and cross-functional teams.
• Opportunities to contribute to architecture discussions and influence technical solutions.
• Continuous learning and professional development through exposure to modern engineering practices and technologies.
• Collaborative agile environment focused on knowledge sharing and technical excellence.
• Opportunity to develop expertise in data platform reliability, performance optimization, scalability, and cost efficiency.
• Inclusive workplace culture focused on diversity, professional growth, and employee development.
• Opportunities to participate in initiatives that support inclusion, belonging, and collaboration across different communities.