Member of Technical Staff 3

ThoughtSpot ThoughtSpot · Data AI · Bangalore, India

This role is for a Member of Technical Staff 3 on the Cloud Platform team, focusing on the development, operation, and reliability of a multi-tenant SaaS platform. Responsibilities include building and maintaining backend systems and cloud infrastructure, implementing features for tenant provisioning and cluster operations, writing production-quality code, debugging production issues, and improving system reliability and operability. The role requires hands-on experience with cloud platforms (AWS, GCP, Azure), infrastructure as code, containerized workloads, backend programming, distributed systems, and observability tools. A key aspect is the mandatory integration and use of AI coding assistants and AI tools in daily workflows to enhance productivity and quality, demonstrating an 'AI mindset' with curiosity, adaptability, and critical thinking.

What you'd actually do

  1. Build and maintain platform components across control plane and data plane
  2. Implement features for tenant provisioning, configuration management, and cluster operations
  3. Write clean, well-tested, production-grade code and participate actively in code reviews
  4. Debug and resolve issues in cloud-native, distributed production environments
  5. Partner with SRE on observability, alerting, and incident response for services you own

Skills

Required

  • 3-5 years of Solid hands-on experience with at least one of AWS, GCP, or Azure — compute, networking, IAM, and managed services
  • Working knowledge of infrastructure as code — Terraform or equivalent — for provisioning and managing cloud resources
  • Comfortable working with containerized workloads — Docker, Kubernetes basics (deployments, services, config maps, RBAC, namespaces)
  • Strong programming skills in at least one backend language — Go, Java, Python, or equivalent
  • Experience building and operating REST or gRPC APIs in production
  • Good understanding of databases — relational and NoSQL — and how to use them reliably at scale
  • Practical understanding of distributed systems — service dependencies, failure modes, retries, timeouts, and basic HA patterns
  • Hands-on with observability — structured logging, metrics dashboards (Grafana, Datadog, or equivalent), and basic alerting; able to diagnose production issues using these tools
  • Awareness of cloud security basics — IAM least-privilege, secrets management, and network access controls
  • Uses AI coding assistants — Claude, Cursor, Copilot — as a regular part of daily workflow for writing, debugging, and reviewing code
  • Comfortable using AI tools to understand unfamiliar codebases, generate boilerplate, draft documentation, and speed up routine tasks
  • Knows to review AI output carefully and apply judgment before committing or deploying
  • 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

Nice to have

  • Exposure to Kubernetes operators, controllers, or CRDs
  • Familiarity with GitOps workflows — ArgoCD, Flux, or equivalent
  • Basic understanding of multi-tenancy concepts — isolation, resource quotas, or tenant lifecycle
  • Experience contributing to or building internal observability dashboards and alerting pipelines

What the JD emphasized

  • Hands-on experience with at least one of AWS, GCP, or Azure
  • Comfortable working with containerized workloads — Docker, Kubernetes basics
  • Strong programming skills in at least one backend language — Go, Java, Python, or equivalent
  • Practical understanding of distributed systems
  • Hands-on with observability — structured logging, metrics dashboards
  • Mandatory and Required Skills for All ThoughtSpot Roles
  • AI literacy and workflow integration
  • Comfortably and confidently integrate artificial intelligence into their daily workflow
  • 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
  • AI Mindset for All Spotters
  • Every role, across every team, is expected to be fluent and comfortable with using AI to do their best work.