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About This Role
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.
- Lead architecture reviews, technology evaluations, and design sessions while documenting key decisions, dependencies, risks, and trade-offs.
- Provide technical direction across engineering teams and ensure solutions align with enterprise architecture, security, privacy, data governance, and responsible AI standards.
- Mentor architects and engineers and promote common data, AI, integration, and engineering patterns across the organization.
What you bring to the table:
- Strong enterprise architecture, analytical, and problem-solving capabilities.
- Ability to connect business opportunities with practical data and AI solutions.
- Strong technical leadership and mentoring skills.
- Strategic thinking with the ability to translate architecture into executable roadmaps.
- Excellent communication skills and the ability to partner with business, engineering, data, and executive stakeholders.
- Ability to operate effectively in a complex, rapidly evolving technology environment.
What’s needed- Basic Qualifications:
- Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related field, or equivalent experience.
- 10+ years of experience designing enterprise data platforms, data products, integration architectures, analytical systems, or high-volume data solutions.
- Deep experience with modern cloud data platforms such as Databricks, Snowflake, BigQuery, or equivalent, including lakehouse and data warehouse architectures.
- Experience designing data architectures supporting analytics, machine learning, generative AI, and intelligent applications.
- Demonstrated experience in marketing, customer, digital commerce, sales, or similar customer-facing data domains.
- Strong experience designing batch, streaming, API, and event-driven data architectures.
- Strong knowledge of relational, dimensional, event, document, and semantic data-modeling approaches.
- Experience building reusable data products, curated data layers, and semantic models for analytical and operational consumption.
- Familiarity with conversational analytics, AI agents, RAG, vector search, and enterprise AI grounding.
- Experience operationalizing machine learning or AI capabilities such as recommendations, propensity, lead scoring, next-best action, or generative AI use cases.
- Experience with enterprise data governance, metadata, lineage, observability, data quality, privacy, security, and access controls.
- Fluency in SQL and working knowledge of Python, Java, Scala, or another language used for modern data and AI engineering.
- Demonstrated ability to lead architecture across multiple teams and communicate complex recommendations to senior business and technology leaders.
What’s needed- Preferred Qualifications:
- Master's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.
- Experience with Databricks Genie, Mosaic AI, MLflow, Unity Catalog, Delta Lake, or comparable data and AI technologies.
- Experience with AI-agent architecture, RAG, enterprise context and grounding, vector databases, agent evaluation, and responsible AI.
- Experience designing semantic models and governed business metrics for self-service and conversational analytics.
- Familiarity with Salesforce, Marketo, Optimove, Adobe, Hightouch, or comparable customer engagement and activation technologies.
- Experience with real-time customer data, decisioning, recommendation systems, experimentation, attribution, incrementality, or closed-loop measurement.
- Experience integrating enterprise data and AI platforms with downstream marketing, digital, sales, and customer-engagement systems.
- Relevant cloud, data, AI, architecture, or security certifications.
We Offer:
- Inclusive culture with associate-led Business Resource Groups
- 22 days of PTO and Holiday Schedule (7 observed paid holidays + 1 floating holiday)
- Online and Retail Discounts, Company Match 401(k), Physical and Mental Health & Wellness programs, and more!
The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors to include the candidate's experience, skill set, education, geography, business considerations, and internal equity. In addition to base pay, this role may be eligible for bonuses, or other forms of variable compensation.
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Salary Context
This $142K-$192K range is below the median for AI Architect roles in our dataset (median: $182K across 44 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 11,520 AI roles we're tracking, AI Architect positions make up 1% of the market. At Staples, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $224,640 based on 245 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($167K) sits 25% below the category median. Disclosed range: $142K to $192K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Staples AI Hiring
Staples has 5 open AI roles right now. They're hiring across AI Architect, AI Software Engineer, AI/ML Engineer. Positions span Framingham, MA, US, Framingham, MA. Compensation range: $192K - $273K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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