Senior Lead Site Reliability Engineer - Cloud Data & Databricks

Role Overview

Job Responsibilities

• Lead a team of SREs designing, implementing, and maintaining a managed AWS Databricks platform.
• Provide engineering and operational support to application and engineering teams.
• Perform platform design, setup, configuration, workspace administration, and resource monitoring.
• Create multi-availability-zone, multi-region, and multi-cloud resiliency strategies.
• Evaluate architectural designs and technical credentials with vendors, startups, and internal teams.
• Improve system observability, alerting, and capacity planning.
• Optimize infrastructure and deployment processes through automation and operational excellence.
• Execute software design, development, and technical troubleshooting.
• Use authorized AI capabilities for reliability design, incident analysis, and requirements traceability.
• Lead AI-assisted reliability workflows with traceability, auditability, resiliency, and security controls.
• Develop secure production code and review or debug code written by others.
• Apply SRE practices, automate recurring issues, and maintain incident response and postmortem procedures.

Required Qualifications and Skills

• Formal training or certification in software engineering concepts and 10+ years of 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.
• Proficiency in Python application development with automated unit testing.
• Experience using authorized AI capabilities to improve reliability workflows, with validation and data-sensitivity awareness.
• Ability to establish safe AI usage practices while maintaining resiliency, security, and auditability.
• Experience developing with Terraform and understanding Terraform Enterprise.
• Experience delivering system design, application development, testing, and operational stability.
• Knowledge of distributed compute frameworks such as Spark, Glue, or MapReduce.
• Excellent troubleshooting, analytical, and communication skills.

Preferred Qualifications and Skills

• Experience with data pipelines using Spark.
• Exposure to AWS and Databricks platform administration.
• Knowledge of containerization with Docker or Kubernetes and orchestration.
• Familiarity with distributed systems and large-scale data processing.

About the Company