GTM Engineer

GTM Engineer

Sentra is the AI Data Readiness platform for enterprises deploying AI at scale. Sentra continuously discovers, classifies, and governs sensitive data across cloud, SaaS, data warehouses, and AI environments - giving security teams complete visibility into what AI systems can access, what they can expose, and where compliance violations exist. Unlike alternatives that copy data to vendor clouds or require customer-managed outpost infrastructure, Sentra provisions ephemeral scanners directly inside the customer's environment, delivering petabyte-scale classification at a fraction of the cost. Trusted by Lyft, SoFi, Munich Re, PennyMac, Glovo and others.

Sentra's revenue engine runs on state-of-the-art AI-enhanced infrastructure that was made possible with GTM Engineering: GTM data syncs continuously into a warehouse. AI pipelines score every open deal daily and write their conclusions back into the CRM. Attribution models run across a hundred thousand touchpoints. A partner deal-registration portal, a content platform and an internal dashboard suite are all in production, deployed through CI with automated rollback. All of it was designed and shipped in-house, in a matter of hours to days, not weeks.

You will be the founding GTM Engineer within the GTM Engineering & RevOps team, where you'll take over production systems, be asked to own them, harden them, and extend them into the parts of the revenue lifecycle we have not automated yet. The GTM team is your user base. You will find where revenue is leaking, build the system that closes the gap, and own both halves: the commercial reasoning about why a deal moves and the AI-systems that enable the team to act on it.

Responsibilities

• Build and enhance the revenue data platform and AI automations and workflows: The CRM-to-warehouse sync, the enrichment and scoring pipelines, revenue forecasting systems. You will operate these processes, improve them, and keep them reliable where the team never second guesses the output.
• Own the full revenue lifecycle systems management: One system from first touch to renewal, from demand capture and lead flow, pipeline and forecast, to renewals and expansion. Not three disconnected stacks with handoffs between them; this is genuine end-to-end ownership.
• Create analytics infrastructure that allows decision-makers to scale the business. Every number we publish is versioned and carries a reason; you will keep it that way.
• Drive Adoption: Building is half the job; you will measure whether reps use what we build, and treat non-adoption as a defect in the system rather than a failure of the rep.

Requirements

• LLM tooling as an engineer, not a consumer. You have built something where an LLM is a component in a pipeline, and you have opinions about how you verified the output. Agentic coding tools (Claude Code, Cursor or equivalent) are part of how this team works.
• 5+ years in GTM operations, sales engineering, revenue operations, marketing operations or sales systems at a B2B SaaS company, preferably at high-growth startups scaling from $10M - $100M+ ARR.
• Data & Git fluency: You can write the joins and window functions behind a pipeline report without help, and you can tell when a number is wrong because the query is wrong. Branches, pull requests, code review, resolving your own conflicts. Everything here ships through version control and CI.
• Practical scripting and API work: Python, TypeScript or JavaScript. You have read API docs, handled auth, paginated a response and dealt with rate limits without supervision.
• Deep Salesforce knowledge: You can design an object model, write and debug a record-triggered flow, reason about sharing and field-level security, and you know when the right answer is a formula field rather than automation.
• Enterprise marketing automation: Marketo strongly preferred (Pardot, Eloqua or comparable considered). Sync behavior, dedupe and lead-to-contact matching are part of the job, not someone else's problem.
• Commercial judgment: You can explain what a stalled deal looks like in the data, and why that matters to a quarter.