About Tekion

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

Director, GTM Engineering

Reports to: SVP of GTM & Growth

Location: Bay Area preferred

"This isn't a traditional GTM Engineering role. It's a commercialization architect role responsible for building the systems, automation, workflows, and operating model that will help Tekion turn product innovation into scalable revenue growth."

Job Introduction

GTM Engineering is an emerging function that sits between Revenue Operations and Software Engineering. This person designs, builds, and operates the data pipelines, enrichment workflows, AI agent deployments, and automated plays that the GTM team runs on. Every major SaaS company scaling AI-assisted GTM motions in 2026 is hiring for this role — it is the operational backbone that makes distribution, sales, and CS teams significantly more productive.

At Tekion, the Director of AI Distribution will build the campaign engine. The Director of GTM Engineering makes that engine — and every other GTM workflow — faster, smarter, and more automated. They define, deploy, evaluate, and optimize AI agent workflows across the GTM org: lead enrichment, outbound personalization, campaign QA, call prep, competitive intelligence, and pipeline analytics.

Key Role & Responsibilities

AI agent workflow design and deployment across GTM — selecting, configuring, and orchestrating agents for lead enrichment, outbound personalization, content generation, campaign QA, and call preparation

GTM tech stack architecture and optimization — owning the integration layer across HubSpot, Salesforce, Databricks, Clay, and emerging AI tooling

Automation of repetitive GTM workflows — replacing manual processes with agent-driven or rule-based systems that scale without adding headcount

Evaluation and QA of deployed agents — monitoring output quality, measuring impact on conversion rates and pipeline velocity, and iterating based on data

Data pipeline and enrichment infrastructure — ensuring GTM teams have accurate, real-time data on accounts, contacts, intent signals, and competitive positioning

Internal tooling and dashboards — building the operational views that let GTM leadership see pipeline health, campaign performance, and agent effectiveness in real time

What This Person Does NOT Own

Campaign strategy, messaging, or creative — that belongs to the Director of AI Distribution and GTM leads

Product engineering or platform infrastructure — this role builds on top of Tekion's platform, not inside it

Sales execution or quota — this role makes sellers more effective, not more numerous

Marketing brand, events, or content strategy — this role automates distribution of content, not creation of it

Basic Qualifications

5–8 years in GTM operations, revenue operations, growth engineering, or sales operations — with a strong technical/builder orientation

Has personally built and shipped automation workflows using tools like Clay, n8n, Zapier/Workato, or custom scripts — not just managed vendors who did it

Deep fluency in AI agent frameworks — understands how to prompt, chain, evaluate, and monitor AI agents in production GTM workflows

Strong data engineering instincts — comfortable with SQL, API integrations, data pipelines, and can work alongside engineering teams without being one