Head of Technology, Intelligence Ventures

Location: New York

This role requires the ability to work lawfully in the U.S. without employment-based immigration sponsorship, now or in the future.

JOB SUMMARY

The Head of Technology serves as the chief technology officer of a new behavioral intelligence platform initiative within Charter Communications. This executive is accountable for the architecture, engineering, and operations of the full technology stack - spanning large-scale data ingestion and processing, the behavioral embedding platform and feature store, ML/AI infrastructure, an agent-to-agent API layer that enables real-time intelligence exchange with external AI systems, and a business-facing agentic UI that allows non-technical users to query and act on household intelligence through natural language.

The Head of Technology will build and lead a high-performance engineering team capable of competing for talent against the world's leading technology platforms, and will serve as the primary technical voice in strategic partner integrations with cloud, data, and AI platform providers. This role sits on the platform leadership team and reports directly to the Head of Intelligence Ventures.

HOW THE HEAD OF TECHNOLOGY MAKES AN IMPACT

• Own the end-to-end technical architecture of the platform - from large-scale network signal ingestion and processing through behavioral embedding generation, feature store construction, and zero-copy intelligence delivery to enterprise partners - ensuring the platform is production-grade, built for household-scale throughput, and designed for long-term extensibility across new signal sources and use cases.
• Lead the build of the platform's cloud-native data and application infrastructure - including ingestion and transformation pipelines, ML/AI compute environments, zero-copy partner access frameworks, and a real-time agent-to-agent API layer that enables external AI systems (marketing agents, commerce agents, customer service agents) to query household-level intelligence and receive contextually grounded responses. Establish the engineering standards, compute architecture, and MLOps practices that will underpin the platform as it scales from initial build through general availability.
• Serve as the technical lead in strategic partner integrations with cloud, data, and AI platform providers, ensuring each integration is architecturally differentiated and aligned with the platform's cloud-agnostic, API-first design principles. Own the technical relationship with external development partners during the initial build phase, and establish the governance frameworks that allow partner access without compromising the platform's data boundaries or competitive architecture.
• Architect and deliver a business-facing agentic intelligence interface - a natural language UI that allows non-technical marketers, planners, and business users to query household behavioral intelligence, surface demand signals, and take action without requiring data or engineering support. This layer must be grounded in the platform's proprietary embeddings and features, designed for accuracy and trust, and built to serve a broad range of enterprise user personas from day one of general availability.
• Build and manage a world-class engineering organization capable of competing for talent with the leading technology and data infrastructure companies. Define team structure, hiring priorities, and engineering culture - balancing internal team development with external development resources as the platform scales from initial build through a full production-ready, commercially deployed product.

QUALIFICATIONS

REQUIRED QUALIFICATIONS

• Deep expertise across the full intelligence platform stack - including distributed data pipelines, ML platform architecture, embedding systems, feature stores, agent-to-agent API design, and LLM-powered application layers - with demonstrated ability to architect and ship all layers as a coherent, production-grade product
• Demonstrated experience architecting and operating consumer data intelligence or data product platforms underpinned by complex machine learning systems and built on modern, cloud-native data infrastructure
• Hands-on proficiency with Snowflake (including Cortex, Native Apps, and data sharing frameworks), cloud data platforms (AWS, Azure, or GCP), and production ML/AI systems at scale
• Experience building agentic AI systems and LLM-powered product interfaces - including agent-to-agent APIs, retrieval-augmented generation architectures, and natural language UIs grounded in proprietary data - with strong product instincts around accuracy, trust, and user experience for non-technical enterprise audiences
• Proven ability to translate complex technical architecture into clear executive and partner-facing communications; comfortable engaging at the C-suite level and in strategic partner negotiations with hyperscalers and technology platforms
• Strong understanding of privacy-preserving data architecture, including differential privacy, de-identification…