Supply Chain Tech Lead - Center of Excellence

How you’ll demonstrate Ownership

As Tech Lead for the Supply Chain CoE, you own the technical spine of Cognite's Integrated Supply Chain offering — knowledge graph, generative AI agents, and command-center UX. You'll act as the subject-matter expert across source-make-deliver, modeling customers' supply chains and showing how AI/UI tools turn an integrated graph into deviation triage and resolution. You're equally comfortable in a C-level value conversation and a technical session on data models or agent skills. The bar goes beyond prompting AI: you'll engineer domain expertise into testable, deterministic playbooks — treating CoE knowledge as a rigorous system with guardrails, ground-truth testing, and staged rollouts.

The Impact you bring to Cognite

Solution & CoE Strategy: Align the CoE on high-impact use cases (e.g., delayed inbound spare → maintenance delay → order risk) and deploy them as demos or customer solutions.

Knowledge-Graph Data Modeling: Lead customer modeling sessions; show how domain data models and complex hierarchies absorb requirements securely and at scale.

Data & AI Architecture: Guide customers on adopting our graph/AI/UI stack, including integration patterns (Snowflake, Databricks, ERPs) and why agents must be grounded in deterministic graph paths.

AI Skill-System Authorship: Turn domain expertise into structured AI skills with fixed parameters, guardrails, and testable anchors, keeping data models and agent logic aligned.

Agentic Tooling & QA: Serve as escalation point for complex graph queries and agent/UI design; own testing and evaluation frameworks, classifying failures rigorously before changing agent logic.

Enterprise Architecture & Value Selling: Support end-to-end architecture reviews and quantify commercial impact (orders at risk, delay penalties, landed costs) for executive business cases.

Partner Enablement & Documentation: Equip partners with reusable demos and accelerators; maintain clear, consistent terminology across models, docs, and AI logic.

Required Qualifications

Deep knowledge of industrial supply chain planning/execution and its links to production, quality, and maintenance; fluency with ISA-95/88 and SCOR frameworks.

3+ years in industrial supply chain (planning, logistics, inventory) and 5+ years in pre-sales, solution architecture, or delivery for enterprise software.

Experience designing industrial information/knowledge-graph data models (temporal relations, typed hierarchies) and integrating structured/unstructured enterprise data.

Hands-on proficiency in Python and frontend (TypeScript/React), with strong engineering discipline (ADRs, automated testing, CI/CD).

Systemic AI/testing mindset: optimized tool routing, rigorous multi-stage evaluation, root-cause debugging, and graceful handling of edge cases — not just prompting.

Excellent client-facing skills, able to translate complex data/AI concepts for audiences from engineers to C-level executives.

Familiarity with modern data platforms (Snowflake, Databricks, Spark/SQL), source systems (ERP/MES/WMS/TMS), and agentic AI/LLM workflow design.

Prior experience in high-growth (startup/scale-up) environments; Bachelor's degree or equivalent practical experience.