GTM (Go-to-Market) Engineer

About Bitsight Bitsight is a global leader in cybersecurity risk and third-party risk management, helping organizations understand, manage, and reduce their cyber exposure and that of their vendors and partners. As we scale our go-to-market motion, we're investing in the systems and automation that let our sales, marketing, and customer success teams move faster and smarter, which is where this role comes in.

About the Role We're hiring our first GTM Engineer to design and build the infrastructure that turns our go-to-market strategy into repeatable, automated execution. You'll sit at the intersection of engineering, data, and revenue strategy, connecting and automating the systems our sales teams rely on every day. AI-native tools are starting to reshape what's possible in GTM, and you'll own that shift for us: what we adopt, what we build ourselves, and what we walk away from.

What You'll Do

• Support our AI-native GTM roadmap: Help continuously evaluate emerging AI-native GTM platforms and tooling, contribute to build-vs-buy-vs-integrate analysis. Lead migration off legacy point solutions as better AI-native alternatives emerge.
• Build and deploy AI-assisted workflows: Apply LLMs and agentic workflows to tasks like account research, call/transcript analysis, personalized outreach, and internal tooling.
• Build and maintain GTM automation: Design workflows for enrichment, prioritization, and sequencing that scale without manual intervention
• Own system integrations: Connect systems such as Salesforce, Clari, ZoomInfo, Gainsight, Snowflake, and Claude so data flows cleanly across the stack
• Engineer data quality and governance: Build validation, deduplication, and enrichment pipelines that RevOps, sales, and marketing can actually trust without double-checking.
• Partner cross-functionally: Work directly with Sales and RevOps leadership to translate business problems into technical systems and explain technical tradeoffs to non-technical stakeholders.
• Experiment and iterate: Treat pipeline generation and conversion as an engineering problem - test hypotheses, measure results, and scale what works instead of adding one-off point solutions.
• Document what you build: Ensure workflows, integrations, and logic are documented well enough that others can understand, trust, and extend them without you.

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