Onbe
Demand Generation Specialist
United States · Contract
Sponsorship not specified$67k-$79kDetected 2 days ago
SQLSEOHubSpotCRM
About the role
- This role is hybrid for candidates located near our offices; they will work onsite 2 days per week at one of our locations: Chicago, Philadelphia or Dallas metro areas.
- If you are not located in these locations, you will be considered a remote.
Responsibilities
- Build, launch, and maintain nurture campaigns across email, chat, and related automation programs.
- Support lead scoring, lifecycle management, routing updates, list hygiene, and campaign QA in HubSpot and connected systems.
- Help manage website updates, including page edits, form checks, CTA updates, and routine QA to ensure the site is working properly.
- Coordinate with agency or contractor partners on assigned web tasks and follow up on timelines and deliverables.
- Partner with Product Marketing to update website copy, campaign pages, and content offers as needed.
- Support SEO, content alignment, and website optimization efforts that improve discoverability and conversion.
- Pull campaign data, validate lists, and help maintain dashboards and reporting for Marketing and Sales stakeholders.
Requirements
- Interest in or experience with website updates and basic optimization (forms, CTAs, landing pages)
Nice to have
- CRM familiarity (Dynamics or similar) a plus
- Our job titles may span more than one career level.
- Hands-on HubSpot experience (required)
Compensation
- The base salary range for this position is budgeted for $66,960 to $78,750 with eligibility for an annual bonus and overtime.
Benefits
- Monitor HubSpot-to-Dynamics sync health, flag issues, and help resolve lead flow or routing errors.
Company info
- on behalf of our clients, as their comprehensive payments partner.
- We transform the way payments are imagined - as an opportunity for innovation, a source of insight to customers, and a way to connect with partners around the globe!
This listing is sourced directly from Onbe's careers page and normalized into a canonical job model.