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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Vice President - Senior Manager of Site Reliability Engineering - **Company:** JPMorgan Chase & Co. - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Big Data, Continuous Integration, Information Engineering, Data Infrastructure, Distributed Systems, MapReduce, Python (Programming Language), Reliability Engineering, Prometheus, Azure Machine Learning, Datadog, Cloud Platform System, Computer Network Technologies, System Availability, Delivery Pipeline, Grafana, Apache Spark, Data Lakes, Kubernetes, Infrastructure Automation Frameworks, Data Analytics, Data Management, Machine Learning Operations, Terraform, Splunk, Data Pipelines, Dynatrace, Docker, Databricks, Programming Languages - **Published:** October 9, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/28094598/Vice-President-Senior-Manager-Site-Reliability-Engineering-New-Jersey-Jersey-City-7463 ## About the Role * Formal training or certification on site reliability engineering concepts and 5+ years applied experience. In addition, 2+ years of experience leading technologists to manage and solve complex technical items within your domain of expertise * Demonstrated experience managing and growing site reliability or platform engineering teams, with direct responsibility for a team of 8 or more engineers * Advanced proficiency in site reliability culture and principles, with a proven track record of implementing SLI/SLO/SLA frameworks, error budgets, incident management, and production readiness practices across large-scale distributed systems and data platforms * Hands-on experience with observability tooling and monitoring strategies, including white and black box monitoring, alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, or Splunk * Experience leading platform engineering efforts on large-scale data platforms and data lake ecosystems, including distributed compute frameworks such as Spark and managed platforms such as Databricks * Proficiency in Python or similar programming languages for automation, platform development, and operational tooling * Experience with containerization and orchestration technologies including Docker and Kubernetes, and infrastructure as code tools such as Terraform * Demonstrated experience leading teams in the safe and effective use of enterprise-authorized AI capabilities within reliability engineering workflows, including validation practices, escalation paths, and awareness of data sensitivity * Proficient with CI/CD practices and related tooling, container orchestration, and troubleshooting common networking technologies and issues * Strong communication and stakeholder management skills, with the ability to translate complex technical concepts into business-aligned outcomes and influence peers and executive partners Preferred qualifications, capabilities, and skills * Experience with AWS platforms and cloud-native infrastructure services supporting AI/ML and analytics workloads at scale * Familiarity with big data ecosystem tools such as Spark, Glue, or MapReduce, and experience supporting data engineering teams in production environments * Experience building and managing CI/CD pipelines and automation frameworks that support platform reliability and engineering velocity * Background in AI/ML platform engineering, including infrastructure support for model training, serving, and monitoring pipelines * Demonstrated contributions to engineering communities through internal forums, communities of practice, or external conferences ## Description Elevate your engineering leadership to unprecedented levels by joining a team of exceptionally gifted professionals and position yourself among the top echelon in site reliability. In this high-impact role, you will guide and shape the future of large-scale data platform reliability - bringing your expertise in site reliability engineering, platform engineering, and AI/ML infrastructure to mentor and lead a team of 8-10 engineers., * Lead, mentor, and develop a team of 8-10 site reliability and platform engineers, fostering a culture of ownership, blameless post-mortems, and continuous improvement through tailored feedback and growth plans * Own and champion non-functional requirements, availability targets, service level indicators, and service level objectives for services supporting large-scale data platforms and AI/ML workloads, ensuring alignment with stakeholders and production readiness standards * Drive the design, implementation, and evolution of observability and reliability frameworks across distributed systems and data platform environments, leveraging tools such as Grafana, Dynatrace, Prometheus, Datadog, and Splunk * Oversee the architecture and operational stability of data platform infrastructure, including Databricks, Spark-based data pipelines, and big data ecosystem tools, ensuring scalability, security, and high performance * Lead reuse-first adoption of enterprise-authorized AI capabilities within site reliability engineering workflows, establishing team standards for traceability, auditability, and alignment to resiliency and security expectations, with human-in-the-loop validation * Drive a culture of continual improvement by encouraging real-time feedback loops, conducting regular team debriefs, and applying objective, data-driven post-mortem strategies that enable teams to learn from both successes and failures * Manage stakeholders and ensure teams deliver projects aligned with compliance standards, risk and security requirements, service level agreements, and business objectives * Contribute to staffing, budget, and resource planning decisions, including hiring, developing, and recognizing engineering talent across the team * Champion site reliability engineering culture and principles across the organization, ensuring teams document and share knowledge and innovations via internal communities of practice, guilds, and engineering forums * Establish and govern CI/CD pipelines, infrastructure as code practices such as Terraform, and automation frameworks to accelerate delivery and reduce operational toil across the platform