Asset & Wealth Management - Engineering - Marketing Technology (MarTech) - Vice President - Richardson

Role Purpose

The VP of MarTech Engineering will lead the strategic technical vision for our marketing and advertising ecosystem. This leader is responsible for identifying and implementing best-in-class software and engineering practices to integrate with advertisement decision engines and campaign management systems. A primary focus of this role is the modernization of our stack through the definition of new standards for AI model integration, ensuring our campaigns achieve maximum relevance and performance across AI-native platforms and traditional digital channels.

Key Responsibilities

1. MarTech Strategy & System Integration

• Ecosystem Architecture: Design and oversee the integration of complex MarTech stacks, including Demand-Side Platforms (DSPs), Supply-Side Platforms (SSPs), Customer Data Platforms (CDPs), and internal advertisement decision engines.
• Best Practice Implementation: Establish engineering standards for "continuous marketing systems" (CI/CD for marketing), moving away from episodic campaigns toward always-on, data-driven execution engines.
• Vendor & Tool Evaluation: Lead the technical due diligence for third-party MarTech software, ensuring seamless interoperability with internal APIs and data structures.

2. AI Integration & Innovation

• AI Standard Definition: Define and enforce technical standards for integrating Large Language Models (LLMs) and agentic AI into the marketing workflow to automate content generation, audience segmentation, and real-time bidding.
• Relevance Optimization: Develop frameworks to increase campaign relevance on AI platforms (e.g., AI search engines, recommendation bots) by optimizing data feeds and model-friendly content structures.
• Predictive Modeling: Partner with Data Science teams to integrate predictive analytics into campaign management systems, enabling automated "next-best-action" decisioning.

3. Campaign Management & Decision Engines

• Decision Engine Optimization: Enhance internal advertisement decision engines to support multi-touch attribution (MTA) and real-time performance adjustments.
• Scalability & Performance: Ensure that campaign management systems can handle high-throughput data streams and execute real-time optimizations without latency.
• Unified Decisioning: Drive the transition toward "Decision Architecture," ensuring that AI agents across different systems query a centralized authority layer for brand rules and budget constraints.

4. Leadership & Governance

• Cross-Functional Collaboration: Act as the primary technical partner to the Chief Marketing Officer (CMO) and product leadership to align engineering roadmaps with business growth objectives.
• Data Privacy & Compliance: Ensure all MarTech integrations adhere to global data privacy regulations (GDPR, CCPA) and internal security standards, particularly regarding AI data usage.
• Team Development: Lead and mentor a high-performing team of engineers and architects specializing in AdTech, data engineering, and AI integration.

Required Qualifications & Skills

Technical Expertise

• Engineering Leadership: 7+ years of experience in software engineering, with at least 5 years in a senior leadership role specifically within MarTech or AdTech.
• AdTech Mastery: Deep understanding of programmatic advertising, real-time bidding (RTB) protocols, and the mechanics of advertisement decision engines.
• AI/ML Proficiency: Proven experience integrating AI models into production environments, specifically for personalization, optimization, or automated content delivery.
• Data Architecture: Strong knowledge of data-to-decision pipelines, identity resolution, and cloud-native infrastructure (AWS/GCP/Azure).

Strategic Leadership

• Innovation Mindset: Ability to anticipate shifts in the marketing landscape, such as the move toward the "Agentic Web" and AI-driven search.
• Stakeholder Management: Exceptional ability to translate complex technical architectures into business value for executive leadership.
• Operational Excellence: Experience managing large-scale budgets and complex technical SOWs with global vendors.

Education

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.

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