AVP, AI Data Engineering, Customer Data Ecosystem

Role Overview

• Lead the strategy and execution of complex and large Data and Analytics portfolio.
• Ensure data architecture and solutions align with enterprise-wide standards for Data, AI and Analytics.
• Effectively communicate strategy, execution progress, and outcomes to diverse stakeholders and promote data capabilities through thought leadership and presentations.
• Responsible for implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions.
• Design, build and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• Define and operationalize ontologies, context graphs, and knowledge graphs across domains to power reasoning, explainability, and decision intelligence.
• Ensure semantic-first AI and Agentic analytics, ensuring LLMs and agents can consume governed business context, metrics, and rules.
• Drive production-scale execution of semantic and knowledge platforms with strong standards for performance, governance, security, and lifecycle management.
• Identify and champion AI augmented productivity improvements across the end-to-end data management lifecycle.
• Stay current with emerging trends in Agentic AI and data engineering and lead proof-of-concepts and early pilots for emerging data and AI augmented technologies to accelerate speed to market.
• Define and implement robust data management frameworks to ensure successful adoption of Enterprise Data Governance and Data Quality practices.
• Effectively manage the budget and financials for the portfolio.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science or a related field
• 12+ years in data engineering, data management and building large-scale data ecosystems
• 7+ years in senior leadership roles managing complex data and AI portfolio with hands-on experience
• Expertise in real-time data streaming, agentic frameworks, Data APIs, vector stores, and RAG architecture, self-serve analytics and AI
• Deep expertise in semantic layer architecture, ontology modeling, and knowledge graph design at enterprise scale
• Experience integrating knowledge graphs with LLMs, RAG pipelines, vector stores, and Agentic frameworks
• Strong understanding of context-aware data engineering and semantic interoperability
• Proven ability to move from strategy → pilot → scaled enterprise capability.
• Strong executive influence and thought leadership in Agentic analytics and AI native data engineering.

Benefits

• Health insurance
• Bonuses
• Flexible work arrangements
• Professional development opportunities