Sr Director, Marketing AI Transformation

Location: Saint Paul

Job Description This leader turns our marketing AI strategy into working capability. The strategy and priorities are set; this role makes them real in production, at the quality bar a company serving legal, tax, and risk professionals demands. You know how modern B2B marketing actually works, across demand generation, campaign and content operations, lifecycle and email, web and personalization, and audience and measurement, and you have built AI and data capability that makes those teams measurably faster and better.

You own AI skills development: the design, build, versioning, and governance of the library of AI skills and agents that power marketing workflows end to end. You lead a team of AI engineers and are a hands-on builder yourself, not solely a manager. Delivering the strategy also depends on Teams you do not own, including engineering and data science functions;

you will partner with and coordinate those Teams as dependencies, setting clear interfaces and holding delivery on track across them. Success is not a longer list of tools or adoption dashboards. It is complete marketer workflows that work, from brief through content through channel, a foundation that is compliant and portable, and clear ownership of quality so AI improves outcomes without introducing regressions.

Our internal AI credibility is a direct extension of our external brand, and this role protects it.

Key Responsibilities Own AI skills development: architect, build, version, and maintain the skills and agents that power marketing workflows, and set the standards others build to. Lead and grow a team of AI engineers, and stay hands-on enough to build alongside them and set the technical bar. Convert the prioritized AI use-case portfolio into shipped, measurable capability against committed timelines and quality standards.

Manage cross-functional dependencies: partner with and coordinate the engineering and data science Teams the strategy relies on, defining clear interfaces, sequencing, and accountability so delivery does not stall between teams. Deliver complete marketing workflows rather than fragments, from brief to channel-ready output across demand generation, content and creative, lifecycle, web and personalization, and audience and measurement. Embed with demand gen, content, web, lifecycle, and analytics teams to learn their workflows first-hand, so what you build fits how marketing operates and actually gets adopted.

Build the compliance foundation first: legal, privacy, accessibility, and content-integrity checks as reusable building blocks that everything else sits on top of. Establish engineering governance: a single accountable owner per skill, versioning and review discipline, monitoring for quality regressions, cost control, and guardrails for higher-risk autonomous use cases. Engineer for portability and avoid platform lock-in, so investment is not stranded by future platform decisions.

Instrument outcomes that matter, including pipeline contribution, hours redeployed, time-to-insight, adoption and quality, and raise the standard for accountability and workflow redesign, not just speed. Partner with enterprise engineering, IT, and commercial and revenue operations so marketing AI integrates cleanly with the wider technology and commercial architecture.

Required Qualifications Hands-on experience architecting and shipping production AI applications: agentic workflows, skill and context engineering, orchestration, retrieval, and tool and API integration. You have built these systems, not just overseen them. Deep fluency in how B2B marketing works and the technology behind it, built by delivering data, analytics, or AI capability for marketing teams. You know the martech and marketing-data stack and the core workflows: demand gen, campaign and content operations, lifecycle, segmentation, web and personalization, and attribution.

A strong technical foundation in data engineering, analytics engineering, business intelligence, or software engineering: data pipelines, modern data platforms and warehouses, and the discipline to build for scale, quality, and governance. Deep data engineering experience counts fully here. A track record of building a capability or platform from scratch and scaling it to real, sustained usage. Experience leading engineers as a hands-on builder-leader, setting technical direction and raising the bar of those around you.

Proven ability to deliver through influence and partnership across Teams you do…