Site Reliability Engineer - Data Platform

Posted 5 months agoViewed

United StatesFull-TimeCrypto

Company:Kraken

Location:United States

Languages:English

Seniority level:Senior, 5+ years

Experience:5+ years

Skills:

AWSDockerPythonSQLApache AirflowBashKafkaKubernetesCI/CDLinuxTerraform

5+ years experience as a Site Reliability Engineer, Infrastructure Engineer, Data Infrastructure Engineer, or similar roles, with a focus on data infrastructure and security.
Experience maintaining real-time data processing technologies (Kafka, Flink, Debezium).
Working experience managing hybrid multi-tenant cloud systems, particularly on AWS.
Experience with Infrastructure as Code tools (Terraform, Terragrunt, Atlantis).
Experience with containerization and orchestration tools (Kubernetes, Nomad, Docker).
Solid understanding of bash/shell scripting.
Proficiency in at least one programming language (preferably Python or JVM languages).
Experience maintaining data-related technologies (Apache Airflow, Apache Spark, DBs, BI tooling).
Experience solving data access management issues at large scale data-lake.
Familiarity with CI/CD deployment pipelines and related tools.
Strong problem-solving skills.
Experience with data-related technologies (databases, data lakes, airflow, spark) is a plus.

Design data governance mechanisms for lakehouse interaction, security, and compliance.
Implement infrastructure for data ingestion, storage, cataloging, and lineage.
Provide a state-of-the-art suite of BI tools.
Guarantee availability, high performance, scalability, and cost efficiency of the data platform.
Implement data infrastructure solutions supporting multiple business units.
Utilize Infrastructure as Code (IaC) with Terraform.
Develop automation scripts using bash/shell scripting.
Enhance and manage CI/CD pipelines.
Implement robust data monitoring and alerting solutions.
Manage role-based access control (RBAC).
Maintain real-time streaming data architecture (Kafka, Debezium).
Ensure timely and accurate processing of streaming data.
Utilize Kubernetes for containerized application management.
Implement incident response procedures and participate in on-call rotations.
Collaborate with teams to understand requirements and implement solutions.
Document architecture, processes, and best practices.
Support AI/ML teams with infrastructure requests.