JPMorgan Chase—Senior Lead Site Reliability Engineer - Cloud Data & Databricks

Job description

Description: Join us to shape the future of data and analytics, leveraging your expertise to deliver impactful technology solutions. Experience career growth and make a difference in a collaborative environment. As a Senior Lead Site Reliability Engineer at JPMorgan Chase within the AIML Data Platforms and Chief Data and Analytics Team, you will develop and deliver advanced technology products focused on data and analytics. You will tackle complex cloud data platform challenges, especially around Data Lake Tools, and work in an agile environment, collaborating with cross-functional teams. You will help drive the firm’s data and analytics journey, ensuring quality, integrity, and security of data, and leveraging AI/ML technologies to support commercial goals. You will contribute to a culture of innovation and operational excellence. Job responsibilities - Lead a team of SREs to design, implement, and maintain a managed AWS Databricks platform, providing engineering and operational support to Application/Engineering teams - Perform platform design, set-up and configuration, workspace administration, and resource monitoring - Create multi-AZ, multi-region, and multi-cloud resiliency strategies for business-critical products and services - Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and technical credentials - Drive continuous improvement in system observability, alerting, and capacity planning - Collaborate with engineering and data teams to optimize infrastructure and deployment processes, focusing on automation and operational excellence - Execute creative software solutions, design, development, and technical troubleshooting - Uses enterprise-authorized AI capabilities within the work environment to accelerate reliability design and operational decisioning (e.g., incident/post-incident analysis and requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements. - Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., testing/validation automation and production readiness), ensuring traceability/auditability, resiliency, and security controls. - Develop secure, high-quality production code, and review/debug code written by others to ensure reliability and correctness. - Apply SRE best practices to improve reliability, scalability, and performance; eliminate or automate recurring issues; and maintain incident response procedures including root cause analysis and postmortems. Required qualifications, capabilities and skills - Formal training or certification on software engineering concepts and 10+ years applied experience - Strong understanding of SRE principles, including SLIs, SLOs, error budgets, and incident management - Experience with monitoring tools, automation frameworks, and CI/CD pipelines - Proficient in Python application program development with use of automated unit testing - Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve reliability engineering workflows with strong validation habits and awareness of data sensitivity. - Ability to set team practices for safe AI usage in operations (e.g., review/approval expectations and escalation paths) while maintaining resiliency, security, and auditability outcomes. - Experience with Terraform development and understanding of Terraform enterprise - Experience in delivering system design, application development, testing, and operational stability - Knowledge of Big Data distributed compute frameworks like Spark, Glue, MapReduce - Excellent troubleshooting, analytical, and communication skills Preferred qualifications, capabilities and skills - Experience in Data pipelines using Spark - Exposure to AWS & Databricks Platform administration - Knowledge of containerization (Docker, Kubernetes) and orchestration - Familiarity with distributed systems and large-scale data processing