Principal Data & AI Solution Architect

Overview

We are seeking an experienced Principal Data & AI Solution Architect to lead the design, assessment, and execution of a strategic enterprise Data & AI platform initiative. This role will be responsible for validating the current Proof of Execution (POE), defining the future-state architecture, and providing architectural leadership as the solution evolves from a successful pilot into a scalable global platform.

The ideal candidate will bring deep expertise across enterprise data architecture, Snowflake, AI/GenAI solutions, AWS, data modelling, and semantic layer design, with a strong ability to advise stakeholders on long-term technology and platform strategy.

This is a highly consultative and influential role focused on architecture, governance, and strategic decision-making rather than hands-on AI model development or engineering.

Key Responsibilities

Architecture Strategy & Leadership

• Review and validate the existing Proof of Execution (POE) and overall solution approach.
• Assess the suitability of the current Snowflake-centric architecture and recommend the optimal future-state platform strategy.
• Design and define a scalable enterprise Data & AI architecture capable of supporting global adoption.
• Establish architectural standards, best practices, and governance frameworks.

Data Architecture & Platform Design

• Design enterprise-scale data models supporting reliability, operational, and business data domains.
• Define semantic layer architecture and data consumption strategies for analytics and AI use cases.
• Ensure data architecture aligns with performance, scalability, governance, and security requirements.
• Develop integration strategies for multiple third-party data sources feeding into Snowflake.

AI & Innovation Advisory

• Provide guidance on leveraging Snowflake Cortex AI, Snowflake Intelligence, and related AI capabilities.
• Define architectural approaches for AI-powered insights, anomaly detection, defect identification, and intelligent agent interactions.
• Recommend how AI agents should access, interact with, and derive value from enterprise data assets.
• Advise stakeholders on emerging AI technologies and enterprise adoption strategies.

Stakeholder Engagement

• Partner with business, technology, and executive stakeholders to align architecture with business objectives.
• Translate complex technical concepts into clear recommendations for senior leadership.
• Lead architecture reviews and key design decision processes.
• Act as the trusted advisor throughout delivery and platform evolution.

Global Platform Enablement

• Guide the transition from pilot/POE to production-scale enterprise deployment.
• Ensure the architecture supports future global rollout and expansion.
• Identify risks, dependencies, and opportunities for continuous platform improvement.

Required Experience & Qualifications

• 10+ years of experience in Data Architecture, Enterprise Architecture, or Solution Architecture roles.
• Demonstrated experience designing enterprise-scale Data & AI platforms.
• Deep expertise with the Snowflake ecosystem, including architecture, data platform design, and AI capabilities.
• Strong understanding of AWS cloud architecture and modern cloud-native data solutions.
• Extensive experience in data modelling, data warehousing, and information architecture.
• Experience designing semantic layers and enterprise analytics architectures.
• Knowledge of AI, GenAI, and intelligent agent architectures from a solution design and advisory perspective.
• Experience working on large-scale transformation or modernization programs.
• Strong understanding of data governance, security, and compliance frameworks.
• Exceptional stakeholder management and executive communication skills.

Preferred Experience

• Experience with Snowflake Cortex AI, Snowflake Intelligence, or similar AI-enabled data platform capabilities.
• Experience defining enterprise AI adoption strategies and AI governance frameworks.
• Experience supporting global platform rollouts across multiple regions.
• Background in manufacturing, reliability engineering, asset management, or industrial data environments.
• Consulting or advisory experience within large enterprise environments.