Today, Gong’s Talent Acquisition team runs on manual processes and disconnected systems — candidate data in one tool, interview data in another, employee data in a third, and no way to see across any of it without someone stitching it together by hand

This role exists to fix that permanently, with a system, not a person doing it manually

You will not recruit. You won’t source candidates, screen resumes, or run interviews

Your job is to build the engine underneath all of that: the automations, integrations, and AI-powered workflows that let recruiters and hiring managers actually see what’s happening and move faster, without you in the loop for every question

This is a 0-to-1 build. There’s no existing talent engineering function here today — you’re standing it up wiring all of our disparate systems into a single governed layer that AI tools can actually reason over

Success looks like shipped systems, not proposals: a dashboard that updates itself, a stuck-req alert that never needed a human to notice it,



an agent that a recruiter or manager can interact with instead of having to manually find the solution

You’ll be evaluated the same way an engineer is evaluated — by what you ship and the measurable difference it makes. Prior recruiting operations experience is useful context, not a requirement

Unify our systems and make them useful. Connect and integrate Workday (HRIS), Greenhouse (ATS), Gem (sourcing), and BrightHire (interview intelligence)

Build the talent intelligence repository. A governed data and context layer that lets AI tools answer questions using our real definitions and data, with proper permissions — not generic or hallucinated logic

Build agentic solutions across the hiring lifecycle for things like:

Automated reporting on time in stage metrics

Real-time visibility into bottlenecks in our workflow

A conversational tool that lets talent leadership and managers self-serve pipeline status

Surfacing previously-qualified candidates already in

📌 Talent Engineer (Madrid)
🏢 Gong
📍 Madrid