SR Data Platform Engineer
New
Remote-first position based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- DockerPythonSQLGCPJavaKubernetesGoSparkScala
Requirements
- Strong programming experience in Python, Scala, Java, or Go.
- Advanced SQL skills and solid understanding of data modeling and query optimization.
- Strong foundation in algorithms, data structures, and scalable system design.
- Hands-on experience with distributed processing frameworks such as Apache Spark or Apache Beam.
- Experience with containerization and orchestration tools like Docker and Kubernetes.
- Familiarity with CI/CD pipelines and modern DevOps practices.
- Experience with monitoring, observability, and maintaining production-grade data platforms.
- Proficiency in workflow orchestration tools such as Airflow, dbt, or Dataform.
- Strong experience with Google Cloud Platform services (e.g., BigQuery, GCS, GKE, Pub/Sub, Dataflow, Composer, Cloud SQL, IAM).
- Experience supporting or managing analytics and BI platforms such as Looker, Tableau, or Metabase.
- Strong problem-solving skills, autonomy, and ability to operate in complex distributed environments.
Responsibilities
- Design and implement scalable data platform solutions, including high-volume data pipelines and architecture decisions impacting multiple downstream teams.
- Develop and optimize distributed data processing workflows using modern frameworks and cloud-native services.
- Conduct deep technical code reviews, focusing on performance, scalability, cost optimization, and maintainability trade-offs.
- Serve as a technical reference for production issues, including debugging data inconsistencies and improving platform reliability.
- Collaborate with cross-functional teams to define data contracts, ensure data quality, governance, and low-latency delivery to consumers.
- Identify technical debt within the platform and lead initiatives to improve architecture, performance, and maintainability.
- Contribute to an AI-driven engineering environment by applying and evolving AI use cases in data platform workflows.
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