Insights Architect, Data & AI

Intuitive•Sunnyvale, CA

2h•$156,800 - $265,500•Onsite

About The Position

Intuitive is seeking an experienced Insights Architect, Data & AI to shape and deliver scalable data, analytics, and AI capabilities that improve business decisions across operations. The initial focus is Service Supply Chain, with the opportunity to apply product and architecture leadership across other business domains over time. The role sits at the intersection of business processes, enterprise applications, data, analytics, and AI. The architect partners with stakeholders to understand workflows and important decisions, then translates business problems into reusable data products and technical solutions. The work spans SAP and o9 as foundational business platforms and Snowflake as a core enterprise data platform, establishing trusted enterprise data, common metrics and KPIs, semantic models, and governed data products for self-service insights, advanced analytics, data science, and AI-assisted decision support. The position calls for someone who can move between business and technical discussions, bring structure to ambiguity, identify patterns across use cases, and influence teams to build durable capabilities rather than isolated solutions. The role may be filled at a different job level depending on business need and the selected candidate’s experience, knowledge, and skills. The position is scheduled as a day shift and is onsite weekly, with the percentage of onsite work defined by the leader. Compensation varies by region and job level; the listed target ranges are $184,500–$265,500 in Region 1 and $156,800–$225,700 in Region 2.

Requirements

At least 8 years of relevant experience in data and analytics architecture, data products, data warehousing, business intelligence, enterprise applications, or related disciplines.
Ability to learn complex business domains and translate business problems into scalable technical solutions and measurable outcomes.
Experience designing and delivering data and analytics products from problem definition through implementation and adoption.
Strong understanding of data architecture, data modeling, ETL/ELT, integration patterns, data quality, lineage, governance, and enterprise data warehousing.
Hands-on proficiency with SQL and modern cloud data platforms such as Snowflake or comparable technologies.
Experience developing metrics, KPIs, dimensional models, and semantic or business-facing data models.
Ability to recognize patterns across business problems and design reusable capabilities rather than one-time solutions.
Demonstrated success working across business, product, application, architecture, data engineering, analytics, and data science teams.
Strong analytical judgment and ability to navigate ambiguity independently, evaluate tradeoffs, and determine a path forward.
Ability to lead technical and business conversations, influence without authority, and build trusted relationships with senior stakeholders.
Excellent verbal and written communication skills, including the ability to distill complex topics for different audiences.
Bachelor’s degree in Computer Science, Information Systems, Engineering, Data and Analytics, Supply Chain, Business, or a related field, or equivalent relevant experience.

Nice To Haves

Master’s degree in a related discipline.
Experience with planning, inventory, fulfillment, execution, service operations, repair, manufacturing, or related operational processes.
Experience with SAP, o9, or comparable enterprise planning and execution platforms.
Experience with Snowflake and modern cloud data architectures.
Experience designing reusable enterprise data products and governed semantic models.
Familiarity with knowledge graphs, ontologies, or related techniques for representing business relationships and context.
Experience enabling advanced analytics, data science, machine learning, or GenAI solutions using enterprise data.
Experience designing capabilities for self-service analysis, investigation, and root-cause analysis.
Experience working in complex application and data transformation environments.

Responsibilities

Develop an understanding of business processes, decisions, workflows, pain points, and desired outcomes across planning, execution, inventory, repair, and related operational areas.
Partner with business leaders and domain experts to frame complex or ambiguous problems, identify root causes, and determine where data, analytics, automation, or AI can improve decisions.
Translate business problems into product capabilities, data and analytical requirements, technical specifications, and scalable solution architectures.
Lead the design of reusable data products that bring together trusted data, business definitions, standardized metrics and KPIs, analytical models, lineage, governance, and semantic context.
Architect capabilities across SAP, o9, Snowflake, and related platforms while balancing near-term outcomes with scalability and maintainability.
Partner with data engineering teams on data models, ETL/ELT pipelines, integration patterns, data quality controls, and analytical structures.
Design and evolve Snowflake semantic models that make enterprise information easier to understand, discover, and consistently use across analytics and AI.
Evaluate knowledge graphs, ontologies, and related semantic approaches to connect business entities, relationships, metrics, and operational context for investigation and root-cause analysis.
Help shift the organization from disconnected datasets and dashboards toward governed, reusable data products and trusted self-service analytics.
Partner with data science and AI teams to provide governed data and business context for advanced analytics, machine learning, and GenAI-enabled solutions.
Identify patterns across requests and turn them into common data products, architecture patterns, semantic models, standards, or reusable capabilities.
Lead cross-functional business and technical discussions, evaluate alternatives and tradeoffs, and build alignment across business, product, application, architecture, engineering, analytics, and AI teams.
Influence without direct authority, challenge assumptions constructively, and guide initiatives from problem definition through design, implementation, adoption, and continuous improvement.
Communicate technical concepts in clear business language and translate business context into technical detail for delivery teams.
Provide architectural guidance and coaching to engineers, analysts, architects, and product teams to improve solution quality and consistency.