Lead Architect

Visa Visa · Fintech · Bellevue, WA

Lead Architect to define and drive the vision for AI-native data foundations, a hybrid platform consolidating company data into a unified, governed, and intelligent data layer. This role sits at the intersection of data architecture, AI/ML, and product intelligence, shaping how data flows to AI-driven insights, powering dashboards, product analytics, AI agents, and executive decision-making.

What you'd actually do

  1. Define the end‑to‑end architectural vision for AI‑native data foundations, spanning cloud data platforms, semantic layers, AI metadata, and consumption layers.
  2. Lead consolidation of disparate data sources (on‑prem, Hadoop, acquisitions, cloud) into a single, accessible, governed data layer.
  3. Design and evolve an AI‑native semantic layer that enables any agent, analyst, or product to discover, query, and reason over data consistently.
  4. Enable agentic and self‑service analytics, including automated insights, metric discovery, product analytics, and “talk‑to‑data” experiences.
  5. Partner with Product, Business, AI and Data Science teams to operationalize AI Data Scientist capabilities for automated visualization, deep‑dive insights, and product intelligence.

Skills

Required

  • Strong architectural mindset, with experience designing large‑scale data platforms, semantic layers, or analytics foundations.
  • Deep understanding of cloud data ecosystems (data lakes, warehouses, streaming, ETL/ELT, metadata, governance).
  • Experience working with or enabling AI/ML and LLM‑based analytics, including agent‑driven or conversational data experiences.
  • Ability to think in systems and abstractions—data models, metrics, semantics, and contracts—not just pipelines.
  • Comfort operating in ambiguity, shaping vision, and driving alignment across engineering, product, and business teams.
  • Strong communication skills—able to explain complex architectures to senior technical and non‑technical stakeholders.

Nice to have

  • Passion for building platforms that enable product analytics, growth insights, and decision intelligence at scale.

What the JD emphasized

  • AI-native data foundations
  • AI agents
  • agentic and self-service analytics
  • AI Data Scientist capabilities
  • responsible AI usage

Other signals

  • AI-native data foundations
  • AI agents
  • semantic layer for AI
  • agentic and self-service analytics
  • operationalize AI Data Scientist capabilities