[Hiring] GTM Engineer @Sentra

Role Description

Sentra's revenue engine runs on state-of-the-art AI-enhanced infrastructure that was made possible with GTM Engineering. 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.
• 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.
• Create analytics infrastructure that allows decision-makers to scale the business. Every number we publish is versioned and carries a reason.
• Drive Adoption: Measure whether reps use what we build, treating non-adoption as a defect in the system rather than a failure of the rep.

Qualifications

• LLM tooling as an engineer, not a consumer.
• 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: Ability to write joins and window functions behind a pipeline report and recognize when a number is wrong.
• Practical scripting and API work: Proficient in Python, TypeScript or JavaScript.
• Deep Salesforce knowledge: Ability to design an object model, write and debug a record-triggered flow, and reason about sharing and field-level security.
• Enterprise marketing automation: Marketo strongly preferred.
• Commercial judgment: Ability to explain what a stalled deal looks like in the data and why that matters to a quarter.