Architect - Data Engineering

Responsibilities:

Domain Architecture Ownership

• Serve as the single architectural point of accountability for data, analytics, and AI solutions within the assigned business domain (Supply Chain & Operations, Merchandizing, Pricing, Commercial Technology and Finance).
• Define and maintain the domain-driven data models and target-state architecture, ensuring alignment with enterprise data platform standards, governance frameworks, and reference architectures.
• Author architecture artifacts including logical architecture, detailed designs, ADRs and represent domain solutions through Architecture Review processes.

AI & Agentic Solution Design

• Translate product intent into architecture and technical direction for AI-augmented workflows. • Design agentic workflows and multi-agent architectures that solve domain business problems, leveraging Google Gemini Enterprise and Vertex AI.
• Establish reusable agentic design patterns for the domain including evaluation frameworks, human-in-the-loop controls, observability, and cost-awareness built in from design.

Data Architecture on Google Cloud Platform

• Architect data solutions on the GCP data stack applying medallion architecture, data products, and semantic layer principles.
• Embed data governance, security, observability, and FinOps considerations into every solution design.

Stakeholder Engagement & Communication

• Communicate fluently across technical, functional, and business audiences from engineering deep-dives to executive readouts.
• Present complex concepts in clear accessible terms and build stakeholder confidence in emerging architecture patterns through storytelling, visuals, and working demonstrations.
• Interface with business domain leaders, be at the forefront of planning and engagement and act as a trusted advisor to Domain Business leaders, proactively identifying where Data and AI solutions can create measurable operational value.

Delivery & Enablement

• Prototype conceptual models that prove out architecture proposals, then execute a structured handover to implementation teams for productionizing, scaling, and support.
• Partner with solution delivery teams, platform engineering, and Run & Support partners to ensure design intent carries through from blueprint to production.
• Conduct architecture reviews, provide design guidance to engineering teams, and uplift domain teams on agentic and AI architecture best practices.

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 8+ years of experience in Data Architecture, Solution Architecture, or senior Data Engineering roles, including 3+ years with demonstrated architectural ownership of enterprise-scale solutions.
• Deep expertise in cloud platforms (AWS, GCP, or Azure). Hands-on experience with the GCP data ecosystem is a strong advantage.
• Demonstrated ability to define and architect modern AI-enabled solutions, including agentic workflows, LLM application patterns, RAG, grounding, prompt and context engineering, model evaluation.
• Proven experience designing agentic workflows and building agentic solutions using Google Gemini Enterprise and/or Vertex AI is a strong advantage.
• Domain knowledge and familiarity with the core processes within relevant business domain(s).
• Exceptional communication and presentation skills, with a proven ability to explain complex architectural concepts to technical, functional, and executive audiences.
• Experience operating in a forward-deployed or embedded architecture model within business domains is an advantage.
• Familiarity with data mesh and data product operating models, semantic layers, and dimensional modeling is an advantage.