Senior Marketing Automation & Insights Manager

SPS Commerce

2d•$84,370 - $130,790•Remote

About The Position

The Sr. Marketing Automation & Insights Manager engineers the data, AI, and automation systems that power segmentation, targeting, and GTM efficiency across Marketing. Working under limited supervision, this role designs strategies for key initiatives, advises cross-functional partners, and mentors others to raise the bar for the team.

Requirements

Bachelor's degree or equivalent combination of education and experience
5-7 years of relevant experience in marketing automation, marketing analytics, or a related field
Proficiency in Python, including production-quality code, testing, and building or consuming APIs
Hands-on experience building with LLMs: prompt engineering, tool and function calling, retrieval-augmented generation, and agentic loops
Advanced knowledge of marketing automation and analytics, typically obtained through advanced education combined with experience; working knowledge of project management practices
Experience mentoring, coaching, or advising other team members
Excellent written and verbal communication skills
Proven competence with Microsoft Office and standard marketing platforms

Nice To Haves

Experience with CRM (Salesforce), marketing automation platforms (MAP), and low-code/automation tools such as Zapier, S
Experience with data governance and quality frameworks
Experience deploying and operating services on a modern cloud or application platform (Atlas, Vercel, AWS, or similar), using Git-based workflows and CI/CD
Familiarity with building or deploying custom GPTs or predictive models
Familiarity with Claude, Claude Code, the Anthropic API, or comparable agentic development frameworks

Responsibilities

Design scalable data models for GTM activation, anticipating data dependencies and downstream impacts.
Advise Marketing teams on data-driven targeting strategy.
Build and maintain MCP (Model Context Protocol) servers that expose SPS marketing knowledge, brand
Design AI solutions for marketing use cases, integrating AI into day-to-day marketing operations.
Design automation architectures across the GTM stack, anticipating scale, error handling, and performance needs.
Design data governance and quality frameworks, anticipating data integrity risks across systems.
Design innovation roadmaps and adoption plans, partnering with stakeholders to prioritize investments.