Senior Growth Operations Manager

New

JobgetherB2B SaaS, cybersecurity

Based in the United StatesFull-TimeManager

SalaryAnnual base salary ranging from $140,000 to $167,500 USD

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Job Details

At least 6 years of Marketing Operations experience in B2B SaaS, cybersecurity, or infrastructure technology.
Experience leading complex cross-functional initiatives and mentoring team members in fast-paced, high-growth organizations.
Advanced expertise in marketing automation and CRM platforms, particularly HubSpot and Salesforce.
Experience with lead routing solutions such as LeanData or equivalent tools.
Ability to design and manage complex integrations and scalable marketing technology architectures.
Advanced SQL skills and hands-on data modeling experience in cloud data warehouses such as BigQuery.
Proficiency in Python or JavaScript, including production-quality scripts and integrations using REST APIs.
Experience designing and managing lead scoring, intent data, and enrichment systems, with knowledge of predictive modeling concepts and evaluation methods.
Practical experience building agents, automations, and workflows using tools such as Claude Code, Cursor, and Clay.
Ability to scope projects independently, assess technical options and trade-offs, build solutions, and deliver results.

Lead the architecture and optimization of the go-to-market technology stack, including integrations between HubSpot, Salesforce, Clay, Hightouch, and BigQuery.
Design scalable data models, integration frameworks, and governance standards, including documentation, monitoring, and quality controls.
Build reusable marketing and growth workflows for lead lifecycle management, routing, and automated handoffs.
Own account and contact data models, scoring frameworks, intent signals, and enrichment systems.
Partner with Data teams to develop and improve predictive models for targeting, prioritization, and growth strategies.
Develop AI-powered agents and LLM-driven workflows for research, data enrichment, automation, and operational orchestration.
Evaluate emerging AI technologies and establish operations-team adoption standards.
Strengthen attribution, predictive measurement, and performance reporting with Revenue Operations and Data teams.
Identify operational inefficiencies, data gaps, and business risks, and translate findings into improvements.
Provide technical guidance, mentor colleagues, and review operational solutions.

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