Sr. Marketing Technology Manager - Demand Systems

LinkedIn•Sunnyvale, CA

9d•$141,000 - $231,000•Hybrid

About The Position

This role will be based in San Francisco, Mountain View, or Sunnyvale. At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. We are seeking a Sr. Marketing Technology Manager, Demand Systems to help define and build the next generation of enterprise demand workflows across LinkedIn Talent Solutions, LinkedIn Sales Solutions, and LinkedIn Marketing Solutions. This is an individual-contributor role for someone who combines demand-generation judgment with systems thinking and hands-on AI capability. You will own projects or workstreams within complex initiatives, deliver end to end with minimal guidance, and help define how enterprise demand should operate across people, channels, data, decisions, and systems. You will translate that future state into executable knowledge, working prototypes, evaluation criteria, and production requirements. You will operate at the intersection of demand strategy, marketing and sales operations, AI workflow design, and enterprise transformation. You should understand how B2B demand is generated and progressed in practice including through email, marketing automation, CRM, seller workflows, and connected enterprise systems. You should be equally comfortable facilitating discovery with senior business leaders, mapping a complex demand workflow with practitioners, codifying decision logic, and using AI-native tools to produce and test a functional solution. Success in this role is measured by priority demand workflows moved end to end, measurable business and operating outcomes, and reusable capabilities adopted across LinkedIn's lines of business.

Requirements

Bachelor’s degree in Marketing, Analytics, Business, or a related field and 4+ years of experience in marketing technology or marketing operations, or equivalent practical experience.
2+ years of experience using AI-native tools including generative AI assistants, CLI-based AI tools, or comparable enterprise AI platforms as an integral part of day-to-day problem solving and delivery (e.g. Claude Code, Github Copilot or comparable platforms).
2+ years of hands-on experience building functional AI-enabled workflows, skills, agents, automations, or business solutions using generative AI, automation, low-code/no-code, or agentic tools.
3+ years of experience leading platform or technology adoption and operationalization.
3+ years of experience applying analytical and systems-oriented problem solving to translate business needs into end-to-end workflows, requirements, or scalable technology-enabled capabilities.
3+ years of experience supporting B2B demand-generation programs, workflows, systems, or operating models.
3+ years of experience partnering across business, product, data, and engineering teams to deliver complex, cross-functional capabilities.

Nice To Haves

7+ years of experience in platform or technology adoption, including substantial experience in workflow design, scaling, and analytical or systems-oriented problem solving.
Deep understanding of enterprise B2B demand and sales motions, along with experience using enterprise marketing and sales platforms such as Salesforce, Microsoft Dynamics, marketing automation, customer data, campaign-management, or internally developed demand systems.
Demonstrated ability to move from strategy through execution by discovering business problems, redesigning end-to-end workflows, translating complex business knowledge into executable assets, building functional AI-enabled solutions, evaluating quality, and measuring outcomes.
Working fluency with structured data, schemas, APIs, system integrations, SQL, version control, testing, and AI evaluation methods, with the ability to build credibly and partner deeply with Engineering.
Strong product and systems judgment, with the ability to balance user needs, business outcomes, technical feasibility, risk, governance, and enterprise reuse.
Experience designing experiments, evaluation frameworks, success metrics, and measurement plans for workflows, automation, or AI-enabled products, including responsible AI and human-review controls.
Proven ability to influence senior leaders, create clarity amid ambiguity, challenge assumptions constructively, and communicate effectively with executive, business, and technical audiences.

Responsibilities

Own the end-to-end discovery, design, build, and business validation of high-priority AI-enabled demand workflows, independently leading workstreams, coordinating dependencies, resolving issues, and delivering defined outcomes across LinkedIn’s enterprise lines of business.
Partner with demand-generation and go-to-market leaders, marketers, sellers, operations teams, and subject-matter experts to translate growth strategies and ambiguous business problems into clear jobs to be done, target outcomes, and prioritized workflow investments.
Design scalable end-to-end workflows and convert operational expertise into executable knowledge, defining triggers, decisions, business rules, handoffs, exceptions, system actions, reusable instructions, and the data, integration, permission, privacy, and governance requirements needed for enterprise operation.
Use approved AI-native tools to directly build functional workflows, automations, reusable skills, and validated agent prototypes; this is a hands-on Builder role rather than a requirements-only or advisory position.
Design evaluations, quality thresholds, exception tests, human-review controls, success measures, and learning plans; partner with Marketing Engineering to move validated solutions into production while retaining accountability for business logic, workflow behavior, evaluation integrity, and business acceptance.
Influence stakeholders across Marketing, Sales, Engineering, Data Science, Product, Legal, Privacy, and Trust to resolve ownership gaps, determine appropriate domain ownership, align on tradeoffs, and standardize reusable workflow patterns, knowledge assets, governance, and Forward Deployed Builder practices.
Maintain strong knowledge of foundational demand and lead-management systems and adjacent Marketing Technology domains; contribute to platform roadmaps and operational health; and create documentation, playbooks, and enablement materials that support consistent adoption and operation.