Architect - Data Engineering
About the role
The Architect - Data Engineering role is responsible for leading the architectural vision and accountability for data, analytics, and AI solutions within specific business domains such as Supply Chain & Operations, Merchandizing, Pricing, Commercial Technology, and Finance. The position involves defining and maintaining domain-driven data models, ensuring alignment with enterprise standards, and authoring architecture artifacts. The architect will design AI-augmented workflows, agentic solutions leveraging Google Gemini Enterprise and Vertex AI, and architect data solutions on Google Cloud Platform, embedding governance, security, and FinOps considerations. The role also requires strong stakeholder engagement, communication, and the ability to prototype and hand over solutions to implementation teams, as well as providing guidance and best practices to engineering teams.
Must have
- 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.
Technologies
- Agentic AI
- AWS
- Azure
- Data Engineering
- Dimensional Modeling
- FinOps
- GCP
- Gemini
- LLM
- Model Evaluation
- Observability
- RAG
- Vertex AI
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