Lead Site Reliability Engineer

Responsibilities:

• Design, build, and optimize cloud and data infrastructure to ensure the high availability, reliability, and scalability of big-data and ML/AI systems to meet customer needs, while implementing SRE principles such as monitoring, alerting, error budgets, and fault analysis.

• Collaborate closely with cross-functional teams, including customers, development, product management, and security teams, to create secure, scalable solutions that support ML/AI workloads and enhance operational efficiency through automation.

• Troubleshoot complex technical problems in production environments, perform root cause analyses, and contribute to continuous improvement efforts through postmortem reviews and proactive performance optimization.

• Lead the architectural vision and shape the team’s technical strategy and roadmap, balancing immediate needs with long-term goals, driving innovation, and influencing the technical direction.

• Serve as a mentor and technical leader, guiding teams and fostering a culture of engineering and operational excellence by sharing your deep knowledge and experience.

• Engage with customers and stakeholders to understand use cases and feedback, translating them into actionable insights and effectively influencing stakeholders at all levels.

• Utilize your strong programming skills to integrate software and systems engineering, building core data platform capabilities and automation to meet enterprise customer needs and roadmap objectives.

• Develop strategic roadmaps, processes, plans, and infrastructure to efficiently deploy new software components at an enterprise scale while enforcing engineering best practices.

Minimum Qualifications

• Ability to design and implement scalable and well tested solutions, with focus on operational efficiency.

• Strong hands-on cloud experience, preferably AWS.

• Infrastructure as a Code expertise, especially Terraform and Kubernetes/EKS.

• Experience building and managing Cloud, Big Data, and ML/AI infrastructure, including hands-on expertise with Hadoop ecosystem components and related technologies such as EMR, Airflow, Spark, PySpark, AWS SageMaker, AWS Bedrock, Gobblin, Kafka, Iceberg, ORC, MapReduce, Yarn, HDFS, Hive, and Hudi.

• Ability to write high quality code in Python, Go, or equivalent programming languages.

Preferred Qualifications

• Solid understanding of Unix/Linux systems, the kernel, system libraries, file systems, and client-server protocols.

• Have experience with architecting software and infrastructure at scale with a sense of ownership and accountability.

• Experience with observability tools including Prometheus (Alertmanager), Grafana, Thanos, CloudWatch, OpenTelemetry, and the ELK stack.

• Certifications: CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer), AWS Certified DevOps Engineer, or equivalent certifications in cloud and security domains.