GTM Engineer

G2 is the world's largest and most trusted software marketplace, and they are seeking a GTM Engineer to enhance their go-to-market engine using AI. The role involves partnering with various teams to identify use cases, prototype workflows, and operationalize successful strategies to drive efficiency and growth.

Responsibilities:

• Map GTM bottlenecks: Partner with RevOps, Sales, Marketing, and CS to identify where AI can reduce manual effort, increase speed, or improve targeting
• Prototype fast: Use GPT-4, Claude, Zapier, Clay, Hex, Notion, and similar tools to quickly ship working workflows
• Run pilots: Deploy in-field with SDRs, AEs, and CSMs. Shadow, iterate, and learn
• Measure ROI: Quantify hours saved, pipeline influenced, and conversion lifts
• Operationalize workflows: Document and transition working flows to GTM Systems, Enablement, or Marketing Ops for scale
• Translate GTM strategy into execution: Operationalize Piper artifacts into real-time workflows that drive pipeline growth and faster prospect follow-up
• Deploy self-serve AI tools: Launch Slack-triggered workflows that reduce prep/admin time by 25–40% and dynamically prioritize accounts
• Automate connected workflows: Improve hand-offs across Salesforce, Salesloft, Gong, and G2 Intent, cutting time-to-action by 20–30%
• Partner across functions: Increase adoption of GTM tools by 60–80% by aligning AI workflows with how reps and CSMs actually work
• Benchmark against best-in-class: Build internal orchestration modeled on leading GTM platforms like Qualified

Requirements:

• 3–5+ years in RevOps, GTM strategy, or growth—preferably in B2B SaaS
• Demonstrated success deploying AI into GTM workflows (lead scoring, pipeline insights, AI summaries, outbound triggers, etc.)
• Hands-on experience with GenAI and automation tools (e.g., GPT-4, Claude, Zapier, Clay, Airtable, Notion)
• Strong data literacy across Salesforce, Snowflake, Looker, and/or equivalent platforms
• Cross-functional operator who collaborates with Sales, CS, and Marketing—not just for them
• Bias for speed: quick iteration, hypothesis testing, and continuous learning