Data Engineering Specialist I – Kafka & AWS
New
J
JobgetherData engineering
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSPostgreSQLKafka
Requirements
- Have solid professional experience as a senior or more experienced backend developer or data engineer.
- Have practical experience with Kafka or another event-driven architecture.
- Have strong knowledge of PostgreSQL and advanced SQL, including stored procedures.
- Have hands-on experience with AWS.
- Have experience with automated testing and software quality practices.
- Have strong programming skills and the ability to develop reliable, maintainable solutions.
- Be able to translate business needs into technical solutions and understand how systems interact.
- Be adaptable and take initiative in an evolving environment where requirements and technical decisions are still being defined.
- Collaborate effectively within a senior engineering team.
- Apache Iceberg, lakehouse platforms, or other modern data architectures are an advantage.
- Familiarity with supply chain operations, demand planning, or Sales and Operations Planning (S&OP) is an advantage.
- Experience designing or implementing business rule engines is an advantage.
- Experience using AI-powered development tools such as Cursor or similar solutions is an advantage.
Responsibilities
- Build and evolve data ingestion pipelines using Kafka and Amazon MSK, consuming analytical data from GCP and transferring it into AWS environments.
- Model, manipulate, and optimize data in PostgreSQL and Amazon RDS using advanced SQL and stored procedures.
- Implement business rules and contribute to the development of rule engines.
- Manage data routing and distribution through event-driven architectures and Apache Iceberg.
- Participate in technical refinement sessions and translate business requirements into practical technical solutions.
- Ensure deliverable quality and reliability through automated testing, attention to metrics, vulnerability awareness, and code reviews.
- Review colleagues' code and contribute to engineering standards and continuous improvement.
- Integrate AI tools into development workflows, critically evaluating their output and generated solutions.
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