AI Native Data and Architect - Senior Manager

Location:

New York

Other locations:
Anywhere in Country

Date:
Oct 21, 2026

Requisition

Location:

Anywhere in Country

At EY, we’re all in to shape your future with confidence.

We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

AI products and agents are only as useful as the knowledge they can reach, interpret, and trust. You will shape the shared foundation that connects enterprise information and expert know-how, giving teams current, permission‑aware context with evidence that users can inspect.

This is a high‑impact role at the intersection of information architecture, semantic systems, retrieval, data engineering, and AI product design. You will turn fragmented information into dependable organizational memory, prove difficult choices in working systems, and create reusable patterns that help teams build differentiated experiences without recreating context, provenance, and access controls for every use case.

Challenger is a new EY program focused on building the firm’s next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones.

Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale.

Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build.

• Set the knowledge direction. Own the target architecture, roadmap, and decision principles for the knowledge, retrieval, and grounding capabilities used by AI products and agents.
• Design organizational memory. Create a federated approach that respects domain ownership while establishing shared meaning, durable interfaces, and clear lifecycle expectations.
• Translate business questions. Work with domain experts to define the concepts, relationships, and boundaries that AI systems need to interpret information accurately.
• Engineer trusted ingestion. Shape reusable patterns that bring varied sources into the platform while preserving authority, lineage, freshness, licensing, and uncertainty.
• Build effective retrieval. Combine search, semantic, graph, and structured methods based on the problem, making relevance, latency, cost, and explainability trade‑offs visible.
• Make evidence inspectable. Ensure context includes citations, provenance, quality signals, and evaluation hooks so users and teams can understand why an answer should be trusted.
• Embed policy in the path. Partner with security, privacy, records, and legal teams to make permitted use, access, retention, and deletion requirements technically enforceable.
• Prove the architecture. Lead lighthouse implementations from prototype through production, using working systems to resolve choices and establish a credible path to scale.
• Operate and improve. Define service objectives, observability, incident learning, and quality measures that keep knowledge capabilities current and dependable after launch.
• Build shared capability. Create reference components and onboarding practices, mentor engineers and stewards, and guide cross‑functional architecture decisions.

• Information architecture:
  Deep understanding of domain modeling, semantics, taxonomies, ontologies, knowledge graphs, and the judgment to choose the…