About the job
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Join out client to architect a groundbreaking "Digital Standard" platform that tackles the climate and biodiversity crisis head-on. In this role, you will reverse-engineer complex sustainability, traceability, and FSC Chain of Custody (CoC) normative standards into structured digital requirements, ontologies, and advanced metadata schemas. You will lead the design of an enterprise-grade Knowledge Graph and build a Modern Data Stack leveraging Microsoft Fabric and Azure AI Foundry to transform reactive compliance into predictive supply chain intelligence.
Details
Role: Data Architect
Allocation: 100% FTE (Full-time)
Work Model: 100% Remote
Responsibilities
Model a sophisticated Knowledge Graph mapping relationships between certified products, high-risk species, and global geographic regions.
Provide architecture leadership by using Microsoft Fabric, Azure AI Foundry, and state-of-the-art LLMs (e.g., Claude, GPT-4) to ingest and harmonize fragmented supply chain data signals.
Build forward-looking data models combining historical traceability logs with real-time geospatial (GIS) data to predict where ecosystem integrity risks might emerge.
Integrate automated AI agents to independently cross-reference digital audit reports and flag supply chain volume mismatches or "ghost" transactions.
Balance vision with a "rapid incremental" delivery roadmap, ensuring architectural layouts translate into functional software releases.
Act as a translator between digital product teams and forestry/certification experts to optimize data landing zones for maximum real-world impact.
Use data visualization and storytelling techniques to communicate complex multi-layered supply chain risks to non-technical stakeholders.
Requirements
8+ years of hands-on experience in Data Architecture or Data Engineering, with a heavy emphasis on complex supply chains (Sustainability, Logistics, or Finance spaces preferred).
Graph Ecosystems: Deep familiarity with Knowledge Graphs (Neo4j, Azure PostgreSQL with Apache AGE, AWS Neptune, or similar), graph neural networks, and ontology design.
Cloud Infrastructure: Expert-level mastery of the Microsoft Azure ecosystem, specifically Microsoft Fabric and Azure AI Foundry.
AI/ML Engineering: Practical experience integrating LLMs and agentic workflows into production data pipelines for automated classification and predictive risk modeling.
nquisitive "builder" mentality—you enjoy drafting the high-level blueprint but are equally excited to roll up your sleeves and dive directly into the data and code.
Highly adaptable, impact-driven, and comfortable navigating through initial project ambiguity.
Geospatial (Plus): Understanding of geospatial (GIS) data structures and how to overlay them onto graph networks is highly advantageous.
