The posting, in Cognite's own words
archived Sep 3, 2026What Cognite is: Relentless to achieve
Cognite operates at the forefront of industrial digitalization, building AI , and data solutions that solve the world’s hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements. We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you’ll feel right at home here. Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.
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.
Read the full posting ↓
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. 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. 5–7 years in industrial supply chain (planning, logistics, inventory) and 5–7 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, ). Systemic AI/testing mindset: optimized tool routing, rigorous multi-stage evaluation, root-cause debugging, and graceful handling of edge cases — not just . 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 / workflow design. Prior experience in high-growth (startup/scale-up) environments; Bachelor's degree or equivalent practical experience.