Lead Data Architect

Visa•Bellevue, WA

2d•Hybrid

About The Position

Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale — tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters — to you, to your community, and to the world. Progress starts with you. Visa’s Technology Organization is a community of problem solvers and innovators reshaping the future of commerce. We operate one of the world’s most sophisticated global transaction networks—processing more than 65,000 secure transactions per second across 80 million merchants, 15,000 financial institutions, and billions of people worldwide. Within Value‑Added Services (VAS), we are building AI‑native data foundations that transform how data is discovered, understood, and activated—enabling product intelligence, growth analytics, and real‑time decisioning across Visa’s ecosystem. The Opportunity We are seeking a Lead Architect to define and drive the vision for VAS Data Foundations—a hybrid, AI‑ready platform that consolidates company wide data into a unified, governed, and intelligent data layer. This role sits at the intersection of data architecture, AI/ML, and product intelligence. You will shape how data flows from raw signals to semantic meaning to AI‑driven insights—powering dashboards, product analytics, AI agents, and executive decision‑making at global scale. The Work Itself Define the end‑to‑end architectural vision for AI‑native data foundations, spanning cloud data platforms, semantic layers, AI metadata, and consumption layers. Lead consolidation of disparate data sources (on‑prem, Hadoop, acquisitions, cloud) into a single, accessible, governed data layer. Design and evolve an AI‑native semantic layer that enables any agent, analyst, or product to discover, query, and reason over data consistently. Enable agentic and self‑service analytics, including automated insights, metric discovery, product analytics, and “talk‑to‑data” experiences. Partner with Product, Business, AI and Data Science teams to operationalize AI Data Scientist capabilities for automated visualization, deep‑dive insights, and product intelligence. Establish architectural standards for data quality, metadata, lineage, observability, governance, and responsible AI usage. Guide platform evolution across data ingestion, ETL/ELT pipelines, scalable data models, metric catalogs, and presentation layers. Translate business and product needs (growth, churn, retention, fraud, performance) into durable data and semantic architectures. Influence and mentor senior engineers and architects, elevating architectural rigor and long‑term thinking across VAS. Essential Functions Act as the principal architect for VAS Data Foundations, owning long‑term architectural coherence across data, AI, and analytics layers. Lead structured discovery with Product and Business stakeholders to align business questions to metrics to data model to semantic models to AI‑ready data products. Ensure platforms are hybrid cloud‑native, AI‑ready, privacy‑preserving, and globally scalable. Drive architectural decisions that balance speed, governance, reusability, and cost efficiency. Identify systemic patterns across data quality, usage, performance, and analytics friction—and drive foundational improvements. This is a hybrid position. Expectation of days in the office will be confirmed by your Hiring Manager.

Requirements

10+ years of relevant work experience with a Bachelor’s degree, or 7+ years with an advanced degree (e.g., Masters, MBA, JD, MD), or 4+ years with a PhD, or 13+ years of relevant work experience regardless of degree.
Strong architectural mindset with experience designing large-scale data platforms, semantic layers, or analytics foundations.
Deep knowledge of cloud data ecosystems, including data lakes, warehouses, streaming, ETL/ELT, metadata management, and governance.
Experience supporting or building AI/ML and LLM-based analytics, including agent-driven or conversational data applications.
Ability to think in systems and abstractions—data models, metrics, semantics, and contracts—not just pipelines.
Comfortable operating in ambiguity, shaping vision, and driving alignment across engineering, product, and business teams.
Excellent communication skills, able to explain complex architectures to senior technical and non-technical stakeholders.
Passion for building platforms that enable product analytics, growth insights, and decision intelligence at scale.

Nice To Haves

12+ years of experience with a Bachelor’s degree, or 8–10 years with an advanced degree (e.g., Masters, MBA, JD, MD), or 6+ years with a PhD, or 15+ years of experience with a Bachelor’s degree, or 12+ years with an advanced degree, or 6+ years with a PhD.

Responsibilities

Define the end‑to‑end architectural vision for AI‑native data foundations, spanning cloud data platforms, semantic layers, AI metadata, and consumption layers.
Lead consolidation of disparate data sources (on‑prem, Hadoop, acquisitions, cloud) into a single, accessible, governed data layer.
Design and evolve an AI‑native semantic layer that enables any agent, analyst, or product to discover, query, and reason over data consistently.
Enable agentic and self‑service analytics, including automated insights, metric discovery, product analytics, and “talk‑to‑data” experiences.
Partner with Product, Business, AI and Data Science teams to operationalize AI Data Scientist capabilities for automated visualization, deep‑dive insights, and product intelligence.
Establish architectural standards for data quality, metadata, lineage, observability, governance, and responsible AI usage.
Guide platform evolution across data ingestion, ETL/ELT pipelines, scalable data models, metric catalogs, and presentation layers.
Translate business and product needs (growth, churn, retention, fraud, performance) into durable data and semantic architectures.
Influence and mentor senior engineers and architects, elevating architectural rigor and long‑term thinking across VAS.
Act as the principal architect for VAS Data Foundations, owning long‑term architectural coherence across data, AI, and analytics layers.
Lead structured discovery with Product and Business stakeholders to align business questions to metrics to data model to semantic models to AI‑ready data products.
Ensure platforms are hybrid cloud‑native, AI‑ready, privacy‑preserving, and globally scalable.
Drive architectural decisions that balance speed, governance, reusability, and cost efficiency.
Identify systemic patterns across data quality, usage, performance, and analytics friction—and drive foundational improvements.