Staff SRE

ThoughtSpot is hiring a Staff Site Reliability Engineer to own the availability, reliability, security, and efficiency of its multi-cloud production SaaS platform. The role focuses on operating Kubernetes-based services and cloud-native data infrastructure, improving capacity and automation, embedding AI into SRE workflows, and leading incident response.

Responsibilities

• Operate a high-scale, multi-cloud (AWS, GCP) SaaS platform — ensuring reliability, performance, and uptime for business-critical production workloads
• Embed AI and Agentic workflows into SRE practice: leverage AI Ops platforms and LLM-powered autonomous agents for anomaly detection, automated triage, runbook execution, and incident summarization to reduce MTTR
• Drive capacity planning and scaling operations — proactively model growth, right-size infrastructure, and implement horizontal/vertical autoscaling strategies to support SaaS growth without reliability regression
• Architect and operate Kubernetes controller frameworks governing both control plane and data plane services; define and enforce operational standards for cluster lifecycle, workload scheduling, autoscaling, and failover
• Own operations of high-scale cloud-native databases and data infrastructure: PostgreSQL/RDS, DynamoDB, MySQL, Elasticsearch/OpenSearch, ElastiCache (Redis/Memcached) on AWS and GCP — including performance tuning, backup/recovery, and incident response
• Lead incident response and blameless post-mortems for P0/P1 events; drive root cause analysis to permanent resolution and prevention — eliminating repeat incidents through systemic fixes, not workarounds
• Define and enforce a culture of automation-first: identify and eliminate toil through self-healing systems, automated remediation pipelines, and infrastructure-as-code (Terraform, Helm, GitOps)
• Participate in on-call rotations for critical cloud infrastructure; serve as a senior escalation point and incident commander during high-severity events
• Achieve quantifiable SaaS operational Excellence measured by related SLI/SLO/SLA

Skills

• B.Tech. degree in Computer Science or equivalent
• At least 6+ years of  Enterprise SaaS Ops experience
• Strong proficiency in programming, particularly with Go and Python, and experience with Infrastructure as Code (IaC) tools like Terraform and Ansible
• Expertise in Cloud Security and/or Cloud networking
• Experience with AI Ops tools, Agentic LLM
• Experience/ Knowledge in Cloud Services, Kubernetes, Cloud Databases like Postgres/RDS/MySQL/DynamoDB, Elastic, Kafka, and Microservice architecture
• Experience in implementing and operating enterprise-grade observability ( metrics, logs, tracing), alerting stack in a Cloud SaaS environment
• Strong debugging and problem-solving skills (network, systems, database, and application)
• Comfortably and confidently integrate artificial intelligence into their daily workflow to increase productivity and quality
• Hands-on experience to leverage AI tools (industry-leading LLMs) to increase productivity, automate routine tasks, and improve work quality
• Speak to the experience of using AI for research, content creation, and document summarization while maintaining ownership of judgment and final decisions
• Write effective prompts to get the most accurate and creative results from AI tools
• Curiosity in exploring new AI tools
• Adaptability to quickly learn and implement new, emerging AI technologies
• Critical thinking to know when to identify when AI should be used versus when human judgement is necessary
• Advanced professional certifications from Cloud Providers ( AWS, Azure, GCP) in domains like K8s, Solution architecture, networking, and databases
• Full Stack Architecture/Development Experience

Benefits

• In-office 3+ days per week, with flexibility available where genuinely needed.
• Training and resources are provided so every Spotter can confidently create with AI.

Company Overview

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