AI Marketing Technology Lead

Avenue Z•Orlando, FL

23h•$75,000

About The Position

The AI Marketing Technology Lead sits at the intersection of marketing, data, and AI engineering. You will own key parts of our martech and analytics stack, ensure data quality and visibility across channels, and design, build, and iterate on AI-powered workflows and internal tools that help Avenue Z teams work faster and smarter. This is a hands-on builder role: you will scope problems with stakeholders, define requirements, and personally ship automations, agents, and analytics capabilities that support marketing and revenue outcomes.

Requirements

3–5+ years in product, marketing technology, analytics, automation, or similar builder roles supporting marketing or growth teams.
Hands-on experience with workflow automation and AI tools (e.g., n8n, Zapier, OpenClaw, Glean, Make, Lovable, Replit).
Strong proficiency with GA4 and GTM: event design, implementation, testing, and attribution reporting.
Experience working with CRM and marketing technology platforms (e.g., Salesforce, HubSpot) with a focus on data quality, reporting, and integrations (not campaign execution).
Solid understanding of digital marketing channels and how they generate and move data (paid media, SEO, content, social, etc.).
Comfortable working in fast-moving, ambiguous environments and shipping iteratively.
Strong analytical skills and ability to interpret data to drive decisions.
Excellent communication skills with the ability to bridge technical and non-technical teams.

Nice To Haves

Experience with BI and visualization tools such as Looker Studio, Tableau, or Power BI.
Working knowledge of SQL for querying marketing and CRM data.
Familiarity with additional martech/automation platforms (e.g., Marketo, Pardot, Klaviyo, or ABM tools like Demandbase or 6sense).
Relevant certifications (e.g., Google Analytics, Salesforce, HubSpot, or AI/automation-related credentials).
Bachelor’s degree in Marketing, Information Systems, Computer Science, Business, or a related field (or equivalent experience).

Responsibilities

Partner with PR, SEO/AIO, content, paid media, strategy, and analytics teams to understand data needs, bottlenecks, and manual workflows.
Translate business questions into clear technical requirements for data models, dashboards, and AI/automation solutions.
Maintain a prioritized backlog of AI and analytics opportunities tied to measurable business impact.
Own implementation and governance for Google Tag Manager (GTM) and GA4, ensuring accurate event tracking, conversions, and attribution.
Design and maintain dashboards and reports that give teams visibility into performance (pipeline, conversion, efficiency, and quality metrics).
Define and enforce standards for UTM tagging, source tracking, and data hygiene across key systems (e.g., Salesforce, HubSpot).
Partner with analytics and finance to ensure data consistency and reliable decision-making across tools.
Design and build agentic AI workflows and internal tools using platforms such as n8n, Zapier, OpenClaw, Glean, Make, Lovable, and Replit.
Implement AI-assisted workflows for research, QA, reporting, content support, and operational checks—focusing on speed, reliability, and clear outputs.
Prototype quickly, test with real users, and move successful builds into stable, monitored production use.
Define prompts, logic, approvals, error handling, and notifications so systems are robust and trustworthy.
Document workflows in visual tools and enable team members on process improvements.
Evaluate, recommend, and integrate tools across the martech and analytics stack to improve data visibility and operational efficiency.
Collaborate with IT, sales ops, and vendors to manage integrations, APIs, and data syncs between platforms.
Document architecture, data flows, and standard operating procedures so systems are understandable and maintainable.
Interview stakeholders and capture product and project requirements
Lead marketing process mapping exercises with department leads
Translate complex technical and analytical concepts into clear, actionable guidance for non-technical stakeholders.
Train teams on new dashboards, tools, and workflows to drive adoption and consistent usage.
Collect feedback from end users and iterate on solutions to improve usability, reliability, and impact.