GTM Engineer

Triple Whale

1d•$105,000 - $125,000•Remote

About The Position

Triple Whale is the source of truth for eCommerce brands. Our platform centralizes the entire analytics stack- from profit tracking and customer insights to marketing attribution and creative intelligence. We empower brands with the visibility they need to make smarter decisions, scale faster, and optimize every dollar spent. The GTM Engineer will play a pivotal role in enabling and optimizing AI-powered workflows across our Go-to-Market (GTM) and Product teams. This role sits at the intersection of business strategy and AI implementation, acting as a bridge between technical capabilities and practical business applications. The primary mission is to drive the adoption and optimization of our AI features across internal teams. This involves implementing and maintaining AI models and data pipelines, monitoring performance, troubleshooting issues, and collaborating with technical and business teams to align AI solutions with organizational goals

Requirements

Experience: 3+ years in operations, Business Intelligence (BI), or a similar role, with demonstrable experience in implementing automation or AI/ML solutions
Education: Bachelor's degree in Business, Computer Science, Data/Information Systems, or a related field.
AI/ML Knowledge: Hands-on experience with generative AI platforms and APIs (e.g., OpenAI, Claude) and a strong understanding of AI models and their application in business.
Technical Skills: Proficiency with data analysis tools, CRM systems (HubSpot), GTM systems (Clay, Avoma, Zapier, etc) and potentially basic programming/scripting knowledge (Python, SQL preferred).
Soft Skills: Excellent cross-functional communication, problem-solving, and change management skills, with the ability to explain complex AI concepts to non-technical stakeholders.

Responsibilities

Cross-Functional AI Strategy & Enablement:
Democratize AI: Embed intelligent automation into daily workflows across all four departments, ensuring teams have the tools and training to leverage AI effectively.
Identify Opportunities: Work with business leaders to pinpoint processes that can be improved or automated with AI (e.g., lead scoring, content generation, churn prediction, product analytics).
Drive Adoption: Lead company-wide AI training workshops and best-practice sharing to foster an AI-forward culture and measure the impact of AI initiatives.
Feedback Loop: Establish a robust feedback loop to translate departmental needs and customer insights into AI feature requirements for the Product and Data Science teams.
Marketing & Sales Operations:
Campaign Optimization: Implement AI agents or AI software to analyze cross-channel marketing data, optimize budget allocation, and provide actionable recommendations to improve ROI.
Sales Intelligence: Use AI to generate customer-centric sales intelligence, enrich CRM data, and automate the creation of personalized pitch materials.
Lead Management: Refine AI-driven lead scoring and routing models to prioritize high-potential opportunities and ensure prompt follow-up, boosting conversion rates.
Product & Customer Success Operations:
Product Insights: Leverage AI to analyze customer feedback, support tickets, and usage patterns to surface insights for product enhancements and roadmap prioritization.
Onboarding & Retention: Design and implement AI-powered workflows to assist with seamless customer onboarding and identify potential churn risks, facilitating proactive retention efforts.
Performance Monitoring: Monitor the performance of AI features within the product and ensure they meet customer needs and business goals.
Technical Operations & Management:
AI System Management: Oversee the day-to-day management, monitoring, and operational support of the organization's AI systems and data pipelines.
Data Integrity: Ensure data quality and seamless data flow between marketing automation, CRM (e.g., Salesforce), and product analytics platforms.
Compliance: Ensure all AI operations and data handling adhere to ethical guidelines, data privacy regulations (e.g., GDPR, CCPA), and company policies.