Manager, Demand Engineer

BeyondTrust•Toronto, ON

About The Position

The Manager, Demand Engineering reports to the SVP, Growth Marketing, and operates as a cross-functional GTM architect embedded in the marketing organization, across Demand Generation, Product Marketing, and Campaign Management. This is a builder role: equal parts operator and architect, with a mandate to ship working systems, not slideware. This person’s mandate is to identify where the revenue funnel breaks or leaks, across the full funnel from top to bottom, and engineer solutions to fix them. That includes top-of-funnel inbound and outbound automation, mid-funnel conversion friction, attribution gaps, and handoff failures between marketing and sales. The fix is not more campaigns; it is redesigning the GTM motion itself: introducing AI-powered orchestration, building the data infrastructure needed to make the system visible, and establishing a new operating model for how BeyondTrust creates and converts demand. This role connects Demand Generation, Product Marketing, and Campaign Management, using AI and GTM orchestration to make the whole system perform at a higher level. This role drives systemic change across three marketing functions rather than owning a single defined area: Demand Generation is the team whose outcomes this role is designed to improve. The role works alongside Demand Gen leadership to diagnose performance gaps, introduce AI-powered workflows, and fix programs that are structurally underperforming. Product Marketing provides the messaging frameworks and competitive context the demand system must be grounded in. This role feeds demand signals back to Product Marketing and ensures demand gen engineering decisions reflect current positioning. Campaign Management handles execution. This role works with Campaign Management to translate GTM Engineering decisions into programs, and to identify which execution patterns need to be redesigned at the system level. Beyond marketing, this role will partner with internal teams, including Revenue Operations, where marketing systems intersect with CRM data and pipeline reporting.

Requirements

5 to 8 years in B2B marketing, demand generation, marketing operations, or GTM systems, with a track record of improving measurable pipeline outcomes.
Thinks like an engineer: approaches demand gen as a system to be designed and optimized, not a campaign calendar to be managed.
Hands-on experience with AI tools in a real GTM context — inbound automation, outbound sequencing, intent signal processing, or similar — integrated into actual workflows, not just piloted.
Deeply data literate: can build attribution models, diagnose full-funnel performance, and identify root causes without needing a translator.
Fluent across the modern Demand stack: ABM platforms, marketing automation, CRM, intent data, AI orchestration tools. Understands how the components connect and where they break, and is hands-on enough to build and debug in these systems, not just navigate the UI.
Comfortable building, whether with no/low-code automation platforms or API integrations, enough to prototype and ship without waiting on engineering.
Strong cross-functional operator: drives change through influence across Demand Gen, Product Marketing, Campaign Management, and sales without direct authority.

Nice To Haves

Cybersecurity or enterprise tech background preferred but not required.

Responsibilities

Analyze the full revenue funnel — top to bottom — to identify where pipeline is leaking, stalling, or failing to convert. This includes top-of-funnel inbound and outbound motion, mid-funnel conversion gaps, attribution blind spots, and marketing-to-sales handoff failures.
Translate funnel analysis into a prioritized engineering roadmap — interventions ranked by pipeline impact and feasibility, with baselines and improvement tracked over time.
Make funnel performance consistently visible and trusted by revenue leadership.
Design and implement AI-powered workflows across the full funnel: inbound lead qualification, outbound sequencing, intent signal processing, lead scoring, content personalization, and pipeline acceleration.
Evaluate and introduce demand orchestration platforms that connect data signals, marketing execution, and sales activation into a coordinated, automated motion.
Build internal playbooks so AI and automation become embedded in day-to-day demand operations, not one-off experiments, and stay ahead of the evolving tooling landscape.
Establish guardrails for safe, governed AI use against marketing and revenue data, including permissioning, read/write boundaries, and human-in-the-loop checkpoints, so automation earns trust as it scales.
Design and continuously optimize BeyondTrust’s demand engine — the integrated system of data signals, technology platforms, AI-powered workflows, and coordinated tactics that produces pipeline at scale.
Define the instrumentation and measurement framework that makes every component of the demand engine visible, attributed, and connected to revenue outcomes.
Engineer durable solutions to systemic misalignments between strategy, execution, and infrastructure, not incremental patches.
Translate ambiguous “we should automate this” ideas into scoped, measurable, and maintainable systems, with a bias toward solutions that are trustworthy over clever-but-fragile.
Serve as the connective layer across Demand Generation, Product Marketing, Campaign Management, and sales, translating insights from each into engineering decisions that improve the system as a whole.
Drive GTM planning cadences that create a shared view of funnel performance, pipeline gaps, and the prioritized GTM Engineering roadmap.
Build trust across functions by delivering insights and solutions that make each team more effective — driving change through influence, not org chart authority.
Build and maintain the attribution models, reporting frameworks, and data pipelines that make the demand engine measurable, accounting for multi-touch, multi-channel buyer journeys.
Use data to identify gaps by segment, region, and product line, and translate findings into engineering decisions.
Own the systems that keep marketing data clean and trustworthy, so downstream reporting and AI outputs are reliable by default and trusted across the revenue organization.
Ensure the demand system is grounded in current product positioning and competitive context, and feed demand signals back to Product Marketing to inform strategy.
Align the Demand Gen Engineering roadmap with product launch timelines across all product lines.