Senior Staff Enterprise Architect, Data

About the Role

We are seeking a Staff Enterprise Architect, Data to lead the strategy, design, and modernization of our enterprise data landscape. This role operates at the intersection of data architecture, engineering, and AI enablement, defining solutions to integrate our Data Lake and Data Warehouse across multi-cloud platforms.

Over the next 12-18 months, you will enable self-service data access and natural language query capabilities for business users. You will architect Master Data Management and data lineage frameworks ensuring AI models operate on high-quality, governed data. You will also evaluate and implement AI-powered tools to automate data quality monitoring and enhance data security.

We're looking to speak with candidates based in the San Francisco Bay Area for our hybrid working model.

Key Responsibilities

• Data Strategy & Roadmap
• Design semantic layer architecture standardizing business metrics enterprise-wide. Define governance guardrails ensuring natural language queries access validated master data sources
• Develop Master Data strategy for Customer and Product domains (phases 1-2), Finance and People to follow. Define golden record requirements, stewardship models, and system-of-record hierarchy. Partner with business owners on master data governance
• Define cross-cloud data integration strategy and reference architecture. Specify patterns (federation, replication, abstraction layer) balancing performance, cost, and data freshness. Document trade-offs and recommend implementations for batch and near-real-time use cases
• Develop 12-24 month data architecture roadmaps for Finance, Sales, Product, and People. Identify capability gaps and recommend technology investments with business value and effort estimates

• Systems Design & Solution Leadership
• Evaluate AI-powered data observability platforms for quality monitoring, pipeline failure prediction, and data classification. Define requirements, lead vendor POCs, and establish integration patterns
• Define data ingestion architecture reducing availability from weeks to 3-5 days (batch) and under 15 minutes (real-time). Specify ELT patterns using CDC where feasible. Document source system constraints and partner with engineering on phased implementation
• Establish build vs. buy frameworks for Data Platform, ETL, Data Quality, and Master Data tooling. Define POC criteria and scoring models. Oversee POC execution and present recommendations with TCO analysis to the architecture review board
• Design data solutions for priority initiatives (customer 360, financial reporting, AI pipelines). Ensure designs address quality SLAs, monitoring, security controls, and operational documentation. Validate through architecture review before implementation
• Apply product thinking to data platforms, treating internal consumers as cust