Site Reliability Engineer

Overview

Site Reliability Engineer for the Network Assurance Data Platform team, operating and scaling AWS infrastructure that supports large-scale data pipelines, ML workloads, cloud-native services, and reliability engineering practices.

What you'll do

• Own and operate scalable, reliable, and cost-efficient infrastructure for the Network Assurance Data Platform.
• Manage, optimize, and improve large-scale data workflows using Apache Airflow, AWS EMR, Spark, and Hadoop-based processing platforms.
• Operate and improve Amazon EKS environments supporting containerized services, ML workloads, and production-grade platform components.
• Build and maintain infrastructure automation using Terraform and other infrastructure-as-code practices.
• Develop Python-based automation, integrations, operational tooling, reporting, and reliability improvements.
• Drive FinOps practices including cost visibility, cost allocation, forecasting, anomaly detection, optimization, and governance.
• Partner with data engineering, ML, platform, finance, and product teams to improve reliability, performance, scalability, and cost efficiency.
• Identify infrastructure bottlenecks, performance issues, inefficient workloads, and cost optimization opportunities.
• Improve observability, alerting, incident response, and operational readiness across data and ML platforms.
• Support capacity planning, right-sizing, autoscaling, storage optimization, and workload efficiency across AWS services.
• Lead technical discussions, influence design decisions, and guide teams toward reliable and cost-conscious architecture.
• Provide senior-level technical leadership, mentorship, and operational guidance to engineers across the team.
• Drive continuous improvement in platform reliability, automation, deployment practices, and operational excellence.
• Operate infrastructure supporting reliable, scalable, performant, and cost-efficient NADP data and ML workloads.
• Improve AWS cost visibility, forecasting, governance, cloud-wastage reduction, and stakeholder reporting.

What you'll need

• Bachelor’s degree or higher in Engineering, Computer Science, or equivalent practical experience.
• 8–10 years of relevant experience in Site Reliability Engineering, DevOps, Cloud Infrastructure, Platform Engineering, Data Infrastructure, or Production Engineering.
• Strong hands-on experience operating production infrastructure on AWS.
• Strong experience with Apache Airflow for workflow orchestration, pipeline operations, scheduling, monitoring, and troubleshooting.
• Hands-on experience with AWS EMR, Spark, Hadoop, or similar large-scale data processing platforms.
• Strong experience operating Amazon EKS or Kubernetes-based environments in production.
• Experience supporting containerized workloads, preferably including ML workloads or data platform services.
• Strong hands-on experience with Terraform and infrastructure-as-code practices.
• Strong Python programming or scripting experience for automation, integrations, operational tooling, and infrastructure workflows.
• Practical experience with cloud cost optimization, FinOps, AWS cost analysis, tagging, budgeting, forecasting, and cost governance.
• Strong understanding of Linux systems, networking, distributed systems, and production troubleshooting.
• Experience with observability tools such as CloudWatch, Prometheus, Grafana, Splunk, OpenSearch, Datadog, or similar platforms.
• Experience with incident management, production support, root cause analysis, reliability improvements, and operational excellence.
• Ability to analyze infrastructure, performance, and cost data and convert findings into clear technical recommendations.
• Strong communication skills with the ability to collaborate across data, ML, platform, finance, and engineering teams.
• Ability to operate independently, drive initiatives end to end, and provide technical leadership in a fast-paced environment.

Nice to have

• Experience working with large-scale SaaS platforms or high-volume data infrastructure.
• Experience with ML infrastructure, model execution platforms, batch processing, or data pipeline reliability.
• Experience optimizing EMR, Spark, Airflow, EKS, storage, and compute workloads for performance and cost.
• Experience with AWS services such as EC2, S3, RDS, IAM, VPC, CloudWatch, OpenSearch, Lambda, ElastiCache, and related cloud-native services.
• Experience with AWS Savings Plans, Reserved Instances, Spot adoption, Graviton migration, storage lifecycle management, and workload right-sizing.
• Experience with cloud cost management tools such as AWS Cost Explorer, AWS CUR, Cloudability, CloudHealth, Kubecost, or similar platforms.
• Experience driving FinOps programs, cost reviews, stakeholder reporting, OKR tracking, and executive-level updates.
• Experience with CI/CD systems, GitHub workflows, Atlantis, or similar deployment automation platforms.
• Experience with Puppet, Ansible, Helm, Argo CD, or other configuration and deployment management tools.
• FinOps certification or equivalent hands-on cloud financial management experience is a plus.
• Ability to influence engineering teams toward cost-aware, scalable, and reliable design patterns.

Details

• Location: Bangalore, India.
• Work mode: Hybrid.