GTM Engineer

Overview

You're Decagon's first GTM engineer, building an internal AI-powered sales intelligence platform. You'll be creating tools and workflows that help the sales team identify target accounts, enrich prospect data, and automate research. This is infrastructure work—you're not selling, you're building the systems that make sellers more effective.

Role Snapshot

Company Context

Stage: Series D ($4.5B valuation - recently announced)

Size: ~300 employees and scaling fast

Growth: Post-Series D acceleration mode, aggressive hiring

Market Position: Well-funded player in AI customer service platform space, competing for enterprise logos like Hertz, Duolingo, Affirm, Rippling, Figma

GTM Reality

What GTM Looks Like at Decagon:

• Selling to enterprise customers (see logo list)
• Likely mix of outbound prospecting and inbound from brand/demo requests
• Customer base includes tech companies, e-commerce, travel, financial services
• At 300 people with this customer list, probably 30-50 person GTM org

Your Role in GTM:

• You're not a seller, you're building the tools sellers use
• Think: internal data platform that combines Clay, AI models, web scraping, workflow automation
• Goal is to help AEs research accounts 10x faster than manually Googling and reading websites

What You'll Actually Do

Time Breakdown

Building/Coding (50%) | Talking to Sales Team (25%) | Testing/Iteration (25%)

Key Activities

• Building data pipelines: You're connecting APIs (Clay, web scrapers, LLM providers like Claude/OpenAI), setting up data enrichment workflows, and building internal dashboards. Lots of Python, API work, and figuring out how to stitch together 5-10 different tools.

• Automating research workflows: Sales needs to know "Which companies just hired a new CX leader?" or "Show me all Series B SaaS companies with 50-200 employees." You build the n8n/Zapier/Gumloop workflows that answer those questions automatically.

• Sitting with AEs to understand needs: You'll spend time shadowing sales calls, watching how AEs research accounts, and identifying what's manual/painful. Then you go build automation for it. Lots of "show me how you do this today" conversations.

• Managing the tool stack: You're the owner of Clay, Parallel, Unify, and whatever other GTM tools they're paying for. You configure them, train people on them, and figure out when to build vs buy.

The Honest Reality

What's Hard

• Ambiguous requirements: Sales will say "I need better data" without being able to articulate what that means. You'll need to translate vague complaints into specific technical solutions.

• Tool sprawl chaos: You're wrangling 5-10 different APIs that all break in different ways. Clay's rate limits, Claude's context windows, scrapers getting blocked—you're the one debugging at 11pm when a workflow fails.

• Being first means no playbook: There's no GTM engineering function yet. You're figuring out what to build, what to buy, what to ignore. No mentor, no established patterns.

• Balancing quick wins vs infrastructure: Sales wants results this week. You know the right answer is building proper data infrastructure. You'll be constantly negotiating between shipping fast and building it right.

What Success Looks Like

• AEs are using your tools daily and can research accounts in 10 minutes instead of 2 hours
• You ship a v1 of the platform in first 90 days that automates at least 3 manual workflows
• Sales leadership asks you to present what you've built to the broader company

Who You're Supporting

Primary Internal Customers:

• Account Executives (enterprise sellers closing $100K+ deals)
• SDRs/BDRs (outbound prospectors booking meetings)
• Sales leadership (need pipeline visibility and account intelligence)

What They Care About:

• Can you help them find new accounts that match their ICP faster?
• Can you auto-generate research briefs so they don't spend 2 hours before every call?
• Can you tell them which accounts are "hot" based on signals (funding, hiring, tech changes)?

Requirements

• You're technical enough to write Python scripts, debug APIs, and build data pipelines without hand-holding
• You've used Clay, Claude API, web scraping tools, and workflow automation platforms (n8n/Zapier/Make)
• You understand how B2B sales works—what AEs do all day, what data they need, what makes prospecting painful
• You're comfortable with 0→1 ambiguity and can figure out what to build when no one knows what they need
• You move fast and ship working prototypes instead of perfect solutions
• Bonus: You've worked in RevOps, sales engineering, or built GTM tools before