GTM Systems Engineer, Rogo

• We are optimizing for real GTM systems experience over years-on-paper. Apex, CPQ, and applied AI are all areas where the bar is shifting fast and few candidates have deep, traditional tenure in all of them; what we actually need is someone who has been the systems owner on a revenue team long enough to have broken things, fixed them, and built the guardrails so they didn't break again
• 5+ years owning GTM systems in a revenue, sales, or RevOps organization, the large majority of it hands-on administering Salesforce as a system of record, not just using it
• Within that, meaningful, recent time owning other GTM tooling beyond Salesforce itself: Gong, Clay, Apollo, Lusha, Nooks, Default, LinkedIn Sales Navigator, DocuSign, or close equivalents, including the integration and access layer between them
• Demonstrated experience building out full GTM tech stacks and owning many parts of them, not just one system in isolation
• Hands-on experience with CPQ tooling, quote-to-cash, and customer lifecycle design, including quoting controls, contracts, entitlements, and renewals. We don't expect deep CPQ platform-engineering tenure; we expect you've owned it in production and understand its logic well enough to configure and troubleshoot it
• Recent, concrete experience leveraging AI and LLMs in GTM systems: health and scoring models, MEDDPICC scorers, account research agents, or comparable production work. This is a newer discipline, so we care far more about depth on one or two real, shipped examples than about breadth of buzzwords. You should be able to walk through what you built, how it was evaluated, and what it changed
• Working familiarity with Apex, SOQL, and Lightning Web Components, not necessarily years as a professional developer, but enough fluency to read, reason about, and validate code so you can use AI coding tools like Claude Code to build, test, and deploy it properly and safely
• Comfort with SQL and enough Python or scripting to build and debug your own pipelines, integrations, and backfills
• Ability to take a vague business problem, scope it into a technical spec, and execute it without a roadmap handed to you
• Strong written communication. You can explain what a system does, why it was designed that way, and what the trade-offs were, to both engineers and GTM leadership
• You thrive in fast-paced environments. You are high-intensity and care a lot about what you do, and you're ecstatic to work at a startup
• You are ambitious. You have fun solving problems that others think are impossible
• You are curious. You find joy in learning about AI, technology, and finance
• You are an owner. You are autonomous, self-directed, and comfortable working with ambiguity
• You are collaborative, organized, thoughtful, and kind

Desirable

• We're hiring a GTM Systems Engineer to build and own the systems layer behind Rogo's revenue engine. This is not a conventional Salesforce Admin seat. You will need those skills, deeply, but the job is to question how companies use GTM technology today and then build what replaces it
• You will own Salesforce end to end alongside the surrounding stack: Gong, Clay, Apollo, Lusha, Nooks, Default, LinkedIn Sales Navigator, DocuSign, and whatever we adopt next. You will own quote-to-cash and CPQ tooling, the contract and entitlement layer, and the customer lifecycle that runs on top of them
• Most importantly, you will build with AI as a first-class tool, not a side project. Health and scoring models, MEDDPICC scorers, account research agents, enrichment and routing agents, MCP servers that let LLMs read and write our GTM data safely
• You will use Claude Code and similar tooling to build, test, and deploy real Apex and Lightning Web Components, with the engineering discipline to do it properly
• You'll report into RevOps leadership and work directly with Sales, Sales Development, Customer Success, Deal Desk, Finance, and Engineering. This is a builder-operator role with wide latitude: you help pick the highest-leverage problem, ship it, and own how it runs
• Own Salesforce end to end: data model, schema, Flows, validation rules, permissions, record pages, DevOps hygiene, and platform reliability across the full revenue lifecycle
• Build and maintain Salesforce customizations in Apex, SOQL, Flow, and Lightning Web Components, with tests, deployment discipline, and documentation that survive you
• Own administration and integration of the surrounding stack: Gong, Clay, Apollo, Lusha, Nooks, Default, LinkedIn Sales Navigator, DocuSign, and new tools as we adopt them
• Architect integrations between Salesforce and the rest of the stack, and own the API, OAuth, and access patterns that make them reliable
• Own the full user access lifecycle across GTM systems: provisioning, role and permission changes, and deprovisioning, with access designed by audience rather than one blanket profile
• Evaluate, pilot, and consolidate tooling. Be opinionated about what earns a seat in the stack and what should be retired
• Own CPQ and quoting controls, including ramp and multi-year structures, approval paths, and the guardrails Deal Desk relies on
• Own the contract and entitlement layer: order form and contract extraction, activated contract records, seat and license rollups, billing structure, and the exception reporting that keeps them honest
• Own renewal, expansion, and churn mechanics in the system: how renewal opportunities are generated, deduplicated, forecast, and reconciled against contract cycles
• Design the customer lifecycle in-system so that pipeline, deployment, health, and renewal all read from the same source of truth
• Design, build, and ship AI capabilities into the GTM motion: account health and scoring models, MEDDPICC scorers and deal inspection, account research agents, enrichment, routing, and call-signal extraction
• Build and extend MCP servers and agent tooling so LLMs can read and write GTM data safely, with the field definitions, permissions, and API structure that agent consumption actually requires
• Stand up evals, monitoring, confidence scoring, and guardrails so every AI capability is observable, reversible, explainable, and measured against a business number rather than uptime
• Exercise judgment about where AI genuinely helps versus where it creates risk, and where a human stays in the loop
• Track the AI and automation landscape and bring the best of it into how Rogo's GTM org operates
• Treat data quality as a production control, not a cleanup project: dedupe, segmentation, domain normalization, and explicit override fields with visible thresholds and reasons
• Design for reporting at capture. Owners, reason codes, confidence tiers, evidence fields, and dedupe keys belong in the schema, not in a downstream join
• Run population-scale backfills properly: audit file plus load file, segmented into ready, review, and skip, validated before anything touches production
• Serve as the primary escalation point for GTM system issues, with a bias toward fixing the root cause in the system rather than the symptom in the record