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Media Buyer

Job in Bellows Falls, Windham County, Vermont, 05101, USA
Listing for: Lumen
Full Time position
Listed on 2026-10-03
Job specializations:
  • Marketing / Advertising / PR
    Digital Marketing
Salary/Wage Range or Industry Benchmark: 85000 - 124000 USD Yearly USD 85000.00 124000.00 YEAR
Job Description & How to Apply Below

Lumen is the trusted network for the AI‑powered world, connecting people, data, and applications through our expansive fiber network and connected ecosystem. We enable secure, high‑performance connectivity across cloud, edge, and AI workloads for enterprises, governments, and communities.

At Lumen, you’ll work on infrastructure customers rely on today and build for what’s next, where performance, security, and resilience matter.

This is a high accountability environment where bold ideas drive real innovation for our customers, partners, and industry. The work is challenging, expectations are clear, and trust is built into how we operate. If you’re ready to take ownership, deliver meaningful impact, and help shape the future of AI‑ready connectivity, join us today.

The Role

The Media Buyer owns the day‑to‑day planning, activation, optimization and measurement of Lumen’s paid social campaigns across an assigned portfolio. They translate campaign strategy into effective audience, budget, bid, placement and creative decisions, independently managing performance within approved investment, brand and measurement par

Applies in-depth paid social expertise and knowledge of adjacent creative, ABM, marketing operations and CRM processes to resolve complex delivery and measurement issues, improves how the work is performed and recommends actions based on performance trends. Leads defined testing and optimization work streams, partner across functions and serve as a resource to colleagues.

The Main Responsibilities
  • Independently build, launch and manage paid social campaigns end to end, including structure, audiences, placements, budgets, bidding, scheduling and quality assurance.

  • Own delivery, pacing, frequency and performance for an assigned portfolio; diagnose creative fatigue, audience saturation, auction and delivery issues, and take corrective action within approved parameters.

  • Resolve complex campaign or measurement problems, including situations where precedent is limited; assess tradeoffs, document decisions and elevate material business, brand or investment risks.

  • Lead the paid social creative testing calendar. Design structured tests across hooks, formats, offers and audiences; interpret results; and convert findings into repeatable recommendations.

  • Brief and partner with Creative on social‑native assets, translating platform performance into clear direction for specifications, aspect ratios, motion, messaging and rotation.

  • Build and govern matched, retargeting, lookalike, interest and suppression audiences in coordination with the ABM program to improve relevance and prevent duplicate account exposure.

  • Maintain campaign measurement integrity, including UTM standards, conversion events, form and lead‑object mapping, and troubleshooting with Marketing Operations.

  • Deliver weekly performance narratives that explain what changed, why it changed, business implications and recommended next actions; connect platform results to available funnel and CRM outcomes.

  • Monitor platform changes, formats and beta programs; recommend and lead practical process or campaign improvements, document best practices and provide guidance to less‑experienced colleagues.

What We Look For in a Candidate
  • Bachelor’s degree in business, Marketing, or a related area.

  • 4+ years experience running paid social campaigns in-house or at an agency. B2B experience is preferred, but strong B2C performance social candidates with analytical rigor are worth interviewing.

  • Hands‑on experience in Linked In Campaign Manager and at least one other social ads manager, with campaigns they personally built and optimized.

  • Demonstrated comfort with daily optimization work, and with reading performance data at small sample sizes without overreacting to…

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