Senior Member of Technical Staff

ThoughtSpot•Mountain View, CA

23h•$100,000 - $120,000•Hybrid

About The Position

About the Role We are looking for a Senior Engineer to join our Cloud Platform team and take ownership of the architecture and evolution of our multi-tenant SaaS platform. You will work at the intersection of backend engineering and cloud infrastructure, designing and building the systems that power our control plane and data plane at scale. This is a high-impact, high-ownership role. You will be a technical anchor for the team, driving architecture decisions, mentoring engineers, and partnering with product and infrastructure leaders to shape the future of our cloud platform.

Requirements

SaaS Platform & Multi-Tenancy Hands-on experience building or operating multi-tenant SaaS platforms at scale silo/pool/bridge models, noisy neighbour mitigation, tenant resource quotas, and lifecycle automation (onboarding, provisioning, off-boarding)
Knowledge of data isolation strategies schema-per-tenant, database-per-tenant, row-level security, and per-tenant encryption at rest
Control Plane & Data Plane Proven experience with control plane / data plane separation and cluster management — Kubernetes operators, CRDs, admission web-hooks, RBAC, and namespace isolation
Understanding of configuration management at scale — GitOps workflows, feature flags, and dynamic config propagation across distributed environments
Cloud & Hybrid Infrastructure Deep expertise in any one of AWS, GCP, or Azure VPC, IAM, managed Kubernetes (EKS/GKE/AKS), IaC (Terraform/Pulumi), and cost optimisation across hybrid environments
Familiarity with service mesh technologies — Istio, Linkerd, or Envoy for traffic management, mTLS, and microservices observability
Distributed Systems & Security Strong grasp of distributed systems fundamentals HA patterns, fault tolerance, observability (OpenTelemetry, Prometheus, Grafana), and resilience testing
Understanding of zero-trust security, secrets management (Vault, AWS Secrets Manager)
AI-Augmented Engineering Actively uses AI coding assistants — Claude, Cursor, Copilot — for infrastructure tasks, runbook generation, incident analysis, and ADR drafting
Able to craft effective prompts for complex engineering problems and critically evaluate AI output; evaluates and drives AI tool adoption across the platform team
Spotters are expected to demonstrate AI literacy and workflow integration to include to ability to: 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.
Spotters are expected to exemplify these key traits and AI Mindset: 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 This combination of curiosity, adaptability, and discernment defines the AI mindset, and it’s required for every role at ThoughtSpot.

Nice To Haves

Exposure to FinOps practices
Hands-on experience building and maintaining observability platforms, centralised logging pipelines, metrics dashboards (Grafana, Datadog, or equivalent), distributed tracing infrastructure, and alert management systems that give engineering teams real-time visibility into platform health
Contributions to open-source infrastructure or platform projects

Responsibilities

Architecture & Design Own end-to-end architecture for the cloud platform, spanning control plane and data plane
Design multi-tenant systems with strong isolation, security, and resource governance
Define platform abstractions that work across hybrid environments AWS, GCP, Azure, and on-premises
Drive architectural reviews, RFCs, and ensure decisions are well-reasoned, documented, and scalable
Control Plane Architect and build systems for tenant provisioning, lifecycle management, and configuration
Design cluster orchestration and management systems that operate reliably at scale
Build APIs and automation that enable self-service for operators and tenants
Ensure control plane is highly available, auditable, and observable
Data Plane Design data path components for high-throughput, low-latency workloads
Build and enforce isolation boundaries between tenants at the data layer
Optimise for performance, reliability, and cost efficiency at scale
Engineering Excellence Set technical direction and coding standards for the platform team
Identify and address systemic risks — reliability, scalability, security, and operability
Partner with SRE and DevOps on observability, incident response, and capacity planning