About the job
Location: San Francisco Bay Area (Palo Alto or San Francisco preferred) or Seattle, WA.
Working Model: Hybrid (in-office presence required).
About the Role
We are on the lookout for a Senior Staff Enterprise Architect, Data to spearhead the strategy, design, and modernization of our enterprise data landscape. This pivotal role sits at the crossroads of data architecture, engineering, and AI enablement, crafting solutions to seamlessly integrate our Data Lake and Data Warehouse across multi-cloud platforms.
In the next 12-18 months, you will empower business users with self-service data access and natural language query capabilities. Your responsibilities will also include architecting Master Data Management and data lineage frameworks, ensuring that AI models function on high-quality, governed data. Additionally, you will assess and implement AI-driven tools to automate data quality monitoring and bolster data security.
Key Responsibilities
- Data Strategy & Roadmap
- Design a semantic layer architecture that standardizes business metrics across the enterprise, while defining governance guardrails to ensure that natural language queries access validated master data sources.
- Develop a Master Data strategy for Customer and Product domains (phases 1-2), with Finance and People to follow. Establish golden record requirements, stewardship models, and a system-of-record hierarchy. Collaborate with business stakeholders on master data governance.
- Define a cross-cloud data integration strategy and reference architecture, specifying patterns (federation, replication, abstraction layer) that balance performance, cost, and data freshness. Document trade-offs and recommend implementations for both batch and near-real-time use cases.
- Create 12-24 month data architecture roadmaps for Finance, Sales, Product, and People, identifying capability gaps and recommending technology investments along 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 that reduces availability from weeks to 3-5 days (batch) and under 15 minutes (real-time), specifying ELT patterns using CDC where feasible. Document source system constraints and collaborate 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 team.
