The opportunity
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.
What you'll do
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
What they're looking for
- + years in IT with 7+ years in Data Architecture, Data Engineering, or Enterprise Architecture roles
- + years across three or more: data architecture, data engineering, database management, analytics, or cloud infrastructure
- Proven ability to architect solutions that bridge Data Lakes and Warehouses: in separate clouds (e.g., AWS, Azure, Google Cloud)
- Hands-on experience with Master Data and data lineage tools. Must have: designed master data models for at least two domains: Customer, Product, Finance, or People
- Experience evaluating or implementing AI/ML tools for data quality monitoring: and automated data classification
- Proven success reducing data latency using CDC, streaming, or real-time integration patterns.
- Proficient in SQL and Python. Experience with modern data platforms: (Snowflake, Databricks, BigQuery, or similar). RAG architectures and vector databases are a plus
- Led architecture for large-scale implementations: CRM, Enterprise Data Platforms, Data Lakes, or ERP systems
