Marketing Operations Manager
at Meta
Location
Menlo Park, CA; New York, NY
Compensation
$124k–$178k USD
Type
full time
Posted
1 weeks ago
Market range · company + function + seniority
p25 · target · p75 · n=31
Posted $178k · well below market
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Job description
The Business Marketing Group connects businesses with their customers on Facebook, Instagram, WhatsApp, and Messenger to help them grow their businesses through solutions like pages, advertising, messaging, and more. We succeed when we help our customers grow their business leveraging our solutions. The Global Marketing Operations team runs campaign execution for business marketing, launching hundreds of campaigns a year across an expanding set of digital marketing channels. We are seeking a Marketing Operations Manager who executes campaigns AI-first, pairing deep expertise in marketing execution platforms with the AI skills to automate that work. This role is about owning operational quality by setting executional standards that can be built at agent scale. The ideal candidate is a digital marketing expert who has already replaced meaningful parts of their own workflow with AI and can show what it produced.
Responsibilities
- Own campaign execution and execution quality for an assigned business area, from brief through launch, across lifecycle, email, in-product messaging, web, and Meta ads
- Use AI as the primary tool for execution work, including build specification, copy and asset variants, audience logic, and QA
- Build, configure, and test AI agents that execute campaign workflows, and own the automation roadmap for the area, reporting results in turnaround time, cost, and error rate
- Pilot new delivery capabilities, then codify what correct execution looks like and the eval rubrics that hold agents to it
- Advise partners at the brief stage on what is executable and at what cost, and translate execution learnings into platform requirements
- Raise the AI capability of the team through documentation, enablement, and coaching
- Find new ways to continually streamline execution processes, including working with XFNs (if needed) to automate handoff
Minimum Qualifications
- 6+ years of experience in marketing operations, campaign management, or marketing automation
- Hands-on build experience in at least two digital marketing channels among email, paid social, in-product messaging, web, and SEM or display, with expert-level depth in at least one execution or ads platform (e.g., Salesforce Marketing Cloud, Marketo, HubSpot, Adobe)
- Demonstrated use of AI to produce real marketing work output, with specific examples of what was automated and the measured effect on time, cost, quality, or volume
- Hands-on experience building, configuring, and evaluating AI agents or LLM-based workflows, including prompt iteration and judgment on where output requires human review
- Experience owning campaign execution at portfolio scale, including QA and trade-off decisions across competing requests
- Experience scaling impact beyond own execution, through standards others adopted, requirements platform teams built, and enablement that raised team capability
- Bachelor's Degree or equivalent work experience in digital marketing Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience supporting B2B marketing efforts or high-volume audience segments at global scale
- Experience leading the transition of a workflow from manual execution to automated or agentic execution
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience connecting AI tools to marketing systems through APIs, integrations, or agent tooling
- SQL fluency or comparable data skills, sufficient to validate audiences and interrogate campaign performance independently
- Comfort working in ambiguity, where the standard does not exist yet and part of the job is writing it
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
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