Senior Site Reliability Engineer - GM Motorsports

• Design and implement reliability practices across the motorsports data platform, including Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets for streaming and analytics workloads
• Ensure reliability and performance of high-throughput streaming and batch data pipelines supporting telemetry ingestion, analytics processing, and simulation workloads using technologies such as Kafka, Flink, and Databricks
• Build and maintain comprehensive observability frameworks including metrics, logs, and tracing across the platform. Develop dashboards, alerts, and automated responses that detect system degradation before it impacts engineering workflows
• Drive the automation of platform infrastructure using Infrastructure as Code (IaC) and platform engineering best practices to enable consistent, reproducible environments across development, testing, and production
• Identify operational friction and eliminate manual processes by implementing self-healing infrastructure, automation frameworks, and developer self-service capabilities
• Own the reliability of data ingestion, transformation, and storage layers, ensuring stable and performant integration across distributed data systems
• Continuously evaluate platform performance and scalability, ensuring the data platform can support high-frequency telemetry ingestion, real-time analytics, and large-scale historical analysis
• Provide mentorship and peer review to engineers across the platform team, promoting strong operational discipline, resilient system design, and high-quality engineering practices

• Proven experience in Site Reliability Engineering (SRE), DevOps, or Platform Engineering supporting large-scale distributed systems
• Strong experience with Linux systems administration and cloud-native infrastructure
• Experience operating high-throughput data platforms or streaming systems (Kafka, Flink, Spark, etc.)
• Hands-on experience with Infrastructure as Code tools such as Terraform or similar frameworks
• Experience implementing observability stacks (Prometheus, Grafana, OpenTelemetry, Datadog, etc.)
• Strong debugging and troubleshooting skills across distributed systems
• Ability to break down complex reliability challenges into clear, implementation-ready initiatives
• A growth mindset and commitment to continuous learning in a fast-paced engineering environment

• Experience supporting data engineering platforms or analytics infrastructure
• Experience with Kubernetes and container orchestration platforms
• Familiarity with stream processing frameworks (Apache Flink, Spark Streaming, etc.)
• Experience with real-time telemetry, simulation, or high-frequency data environments
• Experience implementing reliability practices across multi-cloud or hybrid cloud platforms

Relocation benefits may be eligible