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Apple DevOps Engineer - Big Data Platform 
United States, Texas, Austin 
398271624

16.05.2024
Key Qualifications
  • Experience operating and developing infrastructure and services in public cloud environments (AWS, or GCP).
  • Experience with containers and container orchestration platforms such as Docker, Kubernetes or equivalent.
  • In-depth knowledge and experience in one or more large-scale distributed technologies such as Flink, Spark, Hive, Kafka, Cassandra, etc.
  • Experience in big data storage platforms and query engines with knowledge of cutting-edge technologies like Trino, Hive, Iceberg, Delta Lake, and Hudi.
  • Strong proficiency with Helm and Kustomize for managing Kubernetes applications and configurations through GitOps practices
  • Passionate about operational excellence through proper automation and engineering processes using programming languages such as Go, Python, Java, or other JVM languages
  • Proficient in working with Linux or other POSIX operating systems, shell scripting, and networking technologies.
Education & Experience
BS in computer science with 5-7 years or MS plus 3-5 years experience or related experience.
Additional Requirements
  • • Should be highly proactive with a keen focus on improving the uptime availability of our mission-critical services
  • • Excellent verbal and written communication skills, able to collaborate cross-functionally with program managers and engineering partners
  • • Comfortable working in a fast-paced environment while continuously evaluating emerging technologies
  • • Proficiency with logging and observability technologies such as Prometheus, Grafana, Splunk or similar
  • • Validated software engineering experience and field in design, testing, source code management, and CI/CD practices.
  • • Position yourself as a go-to consultative resource and solution expert for Data Engineers and analysts.
  • • Adaptable to prioritizing multiple issues in a high-pressure environment
  • • Bonus: Design, implementation, and benchmarking of ML/deep learning algorithms