Sr Data & AI Architect II

Sr Data & AI Architect II/Work Model: Onsite, Framingham, MA

Staples is business to business. You’re what binds us together.

Team Summary:
The Enterprise Data & AI team partners across Data Engineering, Data Science & AI, Product, Digital, Marketing, Sales, Analytics, and other technology teams to build trusted, scalable data and AI capabilities. The team establishes reusable architecture patterns and technical guardrails that strengthen security, consistency, engineering quality, and responsible AI adoption across the organization.

Role Summary:
Staples is seeking a senior-level Data & AI Architect to lead the architecture of modern data and AI capabilities supporting marketing, customer engagement, digital experiences, sales, and enterprise decision-making. The Sr Data & AI Architect II will define scalable architecture across cloud data platforms, data products, semantic models, real-time data, analytics, machine learning, generative AI, and intelligent applications. This role will enable capabilities such as conversational analytics, Databricks Genie, AI agents grounded in trusted enterprise data, intelligent decisioning, recommendations, and personalization. The ideal candidate combines deep enterprise data architecture expertise with practical experience applying data and AI within customer-facing domains.

What you’ll be doing:

• Own the current-state, transition-state, and target-state data and AI architecture supporting marketing, customer engagement, digital, sales, and analytics use cases.
• Architect modern data platforms and reusable data products using technologies such as Databricks, Snowflake, cloud services, APIs, event streaming, and semantic models.
• Establish governed data and context architectures that allow analytics, AI models, and AI agents to securely access trusted enterprise data.
• Enable intelligent experiences such as conversational analytics, Databricks Genie, AI agents, recommendations, propensity models, and next-best-action capabilities.
• Design reusable customer, account, marketing, product, sales, and engagement data products that support analytics, AI, personalization, activation, and measurement.
• Define scalable architecture patterns across batch, real-time, streaming, API, structured, semi-structured, and unstructured data.
• Establish standards for semantic models, data contracts, metadata, lineage, observability, data quality, privacy, security, and access controls.
• Partner with Data Engineering and Data Science teams to operationalize machine learning and generative AI through governed data products, model outputs, APIs, and reusable services.
• Translate marketing and customer opportunities into architecture decisions, proofs of concept, implementation roadmaps, and technology recommendations.