GTM Engineer

BackOps-AI•San Francisco, CA

1d•Onsite

About The Position

BackOps is seeking its first GTM Engineer to construct the systems, automation, and data infrastructure essential for enhancing the effectiveness of its go-to-market organization during its scaling phase. This role will operate at the nexus of Sales, Customer Success, and the company's product and data systems. The GTM Engineer will transform complex go-to-market workflows into practical tools that empower the team to expedite account understanding, pinpoint critical customer issues, advance opportunities, and effectively showcase the value BackOps provides. The role is particularly suited to addressing the challenges of selling to large enterprises with intricate structures involving multiple sites, stakeholders, systems, and workflows, where conventional CRM approaches and standard RevOps playbooks often fall short. The engineer will be instrumental in designing the underlying infrastructure for this motion while simultaneously developing immediate, field-oriented tools. A key initial priority will be the creation of a 'value ledger,' a dependable, real-time repository detailing the value BackOps delivers to each customer and workflow, encompassing metrics like hours saved, claims processed, exceptions resolved, and response times improved. This ledger aims to serve as a unified source of truth for customer outcomes, renewals, expansion, and strategic storytelling. This is a hands-on, builder-focused position requiring direct collaboration with GTM leaders and field teams to identify inefficiencies, define ambiguous problems, prototype solutions, implement them rapidly, measure their adoption and impact, and refine successful approaches into robust systems.

Requirements

5+ years of experience in GTM systems, Revenue Operations, technical operations, growth, automation, business operations, or a similar applied technical role supporting a complex B2B organization.
A proven history of building functional tools and systems, with the ability to demonstrate their adoption and subsequent improvements.
Strong technical proficiency in SQL, APIs, data modeling, automation/orchestration tools, and modern AI-enabled development tools. Must be comfortable building solutions, not just configuring existing ones.
Solid GTM and commercial acumen, understanding sales and customer team operations, the impact of information on behavior, and the distinction between technically interesting systems and those that drive revenue.
Capability to structure ambiguous business problems and translate them into systems, processes, and operating models, rather than just executing predefined requirements.
Proficiency in reasoning about complex enterprise data models, including organizations with multiple sites, stakeholders, systems, and workflows.
High degree of initiative and comfort operating in ambiguous environments, working directly with the field to form perspectives, build, test, and iterate without a large established support organization.

Nice To Haves

Experience in an early-stage or high-growth environment, contributing to system establishment before scaling.
Background in supply chain, logistics, manufacturing, or another complex, multi-site enterprise vertical.
Familiarity with a modern GTM technology stack including CRM, cloud data infrastructure, enrichment/orchestration, BI, and related tools.
Experience building with or alongside AI agents, copilots, and LLM-enabled workflows, beyond just using them as an end-user.
Experience in strategy, consulting, or business operations that enhances the ability to structure ambiguous GTM problems, combined with demonstrated technical building expertise.

Responsibilities

Build tools that provide Account Executives, Solutions Engineers, and Customer Success Managers with the necessary context to act swiftly, including details on account structure, locations, systems, stakeholders, workflows, pilots, and recommended next steps.
Create a workflow discovery system capable of processing inputs from discovery calls, customer SOPs, ticket exports, and other sources to generate a structured, ROI-ranked overview of automation opportunities.
Instrument pilots and Proofs of Value (POVs) to enable rapid assessment of progress, adoption rates, conversion risks, customer value, and areas requiring attention.
Develop AI-enabled workflows to minimize administrative tasks post-customer interactions, such as drafting follow-ups, extracting action items and product feedback, and proposing CRM updates for review.
Identify expansion opportunities by correlating product usage, customer outcomes, account context, and workflow signals to guide the field on where and why to deepen engagement.
Design the GTM data model tailored for complex enterprise customers, encompassing parent/account/site relationships, buying groups, workflows, pilots, ownership, and stage logic.
Construct and maintain the value ledger, linking product activity and operational outcomes to a verifiable measure of value realized at both the account and workflow levels.
Establish a reusable context layer that fuels GTM tooling with reliable information on accounts, stakeholders, product usage, workflows, and suggested actions.
Implement data quality observability across the GTM technology stack, monitoring for completeness, freshness, duplicates, orphaned records, outdated ownership, and integration failures.
Collaborate with GTM leadership to determine where system, automation, process, or operating model adjustments can enhance productivity and scalability, rather than merely adding more tools.