Staff Forward Deployment Engineer

Mode Analytics

Mountain View, CA$154k–$220kPosted Jul 29, 2026

About the RoleThoughtSpot is building the Agentic Analytics Platform — a governed, AI-native infrastructure that lets anyone ask a question in plain language and get a trusted, traceable answer, powered by Spotter and our semantic layer. Our platform is only as good as its ability to work inside the messy reality of a customer's stack: legacy warehouses, custom auth, bespoke data models, and workflows nobody wrote down. That's the gap this team closes. We're building a new Forward Deployed Engineering (FDE) function inside Engineering, and we're looking for a founding Senior MTS to help shape it. As an FDE at ThoughtSpot, you'll embed directly with our most strategic customers to design, build, and ship agentic analytics solutions on top of Spotter — turning a "great demo" into a production system a customer's team actually relies on. You'll operate like a hands-on technical lead for each engagement: part software engineer, part solutions architect, part product partner, with a direct line back into our core roadmap. This is not a pre-sales or support role. FDEs write production code, own outcomes, and stay until the system works. What You'll DO:<ul><li>Embed with customers to understand their real data environment, business logic, and workflows — then design and build solutions using ThoughtSpot's platform (Spotter, Spotter Semantics, SpotterViz/Model/Code, Embedded, and the Agentic MCP Server) that solve their actual problem, not the idealized version of it.</li><li>Build and ship production code — integrations, connectors, custom agents, and semantic models — writing tested, reviewed, well-documented code that runs inside customer environments under their security, governance, and compliance constrains.</li><li>Write technical design docs and go through code review like any other engineering team at ThoughtSpot: propose an architecture, get it reviewed, ship it, instrument it, and iterate based on real usage.</li><li>Own the full delivery lifecycle: discovery, technical design, implementation, rollout, and enablement of the customer's own team so they can operate and extend what you built without you.</li><li>Navigate the "integration wall": legacy SQL and warehouse systems, enterprise SSO/SAML, data residency requirements, and the operational politics of getting production access — and solve it with working code, not slideware.</li><li>Design and evaluate agentic workflows: prompt and context design, retrieval and grounding against governed semantic models, evaluation frameworks, and guardrails that keep answers deterministic and auditable in production.</li><li>Contribute directly to ThoughtSpot's core codebase: you'll see the same unsolved problem across three customers before Product does — harden that one-off solution into a reusable, tested feature that ships to the broader platform, not just to a single customer</li><li>Communicate across audiences — from a deep technical debugging session with a customer's data engineering team, to a VP or CTO who needs a clear, non-technical explanation of what a solution does and why it's trustworrthy.</li><li>Travel to customer sites occasionally for select engagements (up to ~25%) — most work is done embedded remotely, and some engagements require no travel at all.</li></ul> What We're Looking For:<ul><li> 8+ years of professional software development experience, with a track record of owning complex, ambiguous technical problems end-to-end rather than working from fully specified requirements.</li><li>Strong engineering fundamentals: fluency in at least one of Python or TypeScript/JavaScript, solid data skills (SQL required; experience with large-scale or distributed data systems a plus), and comfort working across cloud platforms (AWS, GCP, or Azure) and containerized environments (Docker/Kubernetes).</li><li>Demonstrated ability to work directly with customers or cross-functional stakeholders — whether that's as an engineer who regularly sat in customer/product conversations, a tech lead who owned client-facing delivery, or someone who has held a customer-facing technical role.</li><li>Product and systems thinking: you can decompose a vague, high-stakes problem into a shippable MVP, and you know the difference between "worked in the demo" and "will survive production".</li><li>Genuine curiosity about applied AI: hands-on experience or strong self-driven learning with LLM-based systems — prompt/context engineering, RAG or grounding against structured data, agent orchestration, or evaluation of model outputs. You don't need to have shipped an AI product to have this; you need to have gone deep on your own.</li><li> Radical ownership and comfort with ambiguity: FDE engagements rarely come with a clean spec. You're comfortable asking the clarifying questions, proposing a simple path forward, and iterating in the open with a customer.</li><li>Excellent written and verbal communication: you can hold your own in a whiteboard session with a customer's principal engineer and then explain the same solution clearly to a non-technical exec.</li></ul> Good To Have: Because Forward Deployed Engineering is a genuinely new discipline, we don't expect most candidates to walk in with years of a job literally titled "FDE" — and we're not filtering for that title. Any of the following are strong signals, but none are required. <ul><li>Prior experience as a Forward Deployed Engineer, Production Engineer, Applied AI Engineer, a hands-on CTO or technical lead at an early-stage startup (where you likely wore this hat without the title), or a technical consultant who implemented AI, ML, or intelligent-automation solutions for clients — agentic experience specifically is a bonus.</li><li>Experience fine-tuning or evaluating LLMs, building with agent frameworks, or working with vector databases and RAG pipelines in production.</li><li>Background in enterprise data/BI, semantic layers, or analytics platforms.</li><li>Experience navigating enterprise security reviews, SSO/SAML integration, or data governance requirements as part of a technical rollout.</li><li>A history of turning one customer's edge case into a feature that shipped to everyone.</li></ul> How We Think About This Team:We're deliberately building FDE inside Engineering, reporting through our VP of Engineering, and not as a renamed sales or support function. That's intentional: your success is measured by whether customers reach production value quickly and by what you feed back into the core product — not by deal velocity or ticket volume. You'll work closely with Product, Applied AI/ML, and Customer Success, but you'll ship code in both the customer's environment and ThoughtSpot's own codebase.Expect a small, senior team, high autonomy, and outsized influence on how this function is built — the playbooks, tooling, and success metrics for ThoughtSpot's FDE org will be shaped by the first people who join it.