AVP, AI Data Engineering, Customer Data Ecosystem

Requirements

• Mastery level data engineering and architecture skills, including deep expertise in data architecture patterns, lakehouses, data integration, data domains, data products, conversational business intelligence, and cloud technology capabilities.
• Mastery in implementing scalable AI driven data systems supporting agentic solutions (AWS Lambda, S3, EC2, Langchain, Langgraph, MCP, A2A).
• Technical expertise in LLMs, AI platforms, prompt engineering, LLM optimization, Retrieval-Augmented Generation (RAG) architectures and vector database technologies (Vertex AI, Postgres, OpenSearch, Pinecone etc.).
• Experience in multi cloud environment.
• Experience in Lang c

Benefits

Additional Information

AVP Data Engineering - GE05AE We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future. As an AVP of AI Data Engineering for Customer Data Ecosystem (CDE), you will be responsible for defining and advancing the consumer AI-ready data architecture that enables agentic analytics, GenAI applications, and differentiated customer and operational experiences. This role ensures data foundations, context, and engineering patterns are scalable, secure, reusable, and aligned across CDE, applied AI, and enterprise architecture partners. This role will lead a small, high-leverage team of architects, semantic engineers, and innovation team, and serves as the senior technical authority for AI-ready data design within CDE. This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Must be eligible to work in the US without company sponsorship. Primary Job Responsibilities Strategy and Execution: Lead the strategy and execution of complex and large Data and Analytics portfolio. Architecture and Solution: 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. AI Data Engineering leader responsible for Implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions. Real-Time Data Streaming: 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. Lead the design and execution of enterprise-scale semantic layers to standardize business meaning and enable trusted analytics, AI, and Agentic use cases. Enable 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. Drive best practices in AI data engineering by establishing standardized processes, promoting cutting-edge technologies, and ensuring data quality and compliance across the enterprise. Leadership: Build, mentor, and lead a high-performing team including Directors, Business data analysts, Data engineers and Release train engineers. Drive efficiency and Productivity: Identify and champion AI augmented productivity improvements across the end-to-end data management lifecycle. This includes researching and implementing innovative solutions such as AI-driven auto-generation of data pipelines, advanced DevOps practices for data and automated data quality frameworks. Technology Evaluation & Adoption: 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. Data Governance, Stewardship and Quality: Define and implement robust data management frameworks to ensure successful adoption of Enterprise Data Governance and Data Quality practices. Budget Management: Effectively manage the budget and financials for the portfolio. Develop deep partnerships and alignment with the portfolio and agile value stream frameworks. Experience with Agile at Scale and iterative development through cross-functional teams. Partners with Technology, Data, AI COE, Applied AI and Architecture teams to influence technology, data, platform and tooling strategy. Evangelize Agentic Data Engineering, driving adoption through patterns, playbooks, and real-world deployments across the enterprise.

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Company Intel

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