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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Context Engineer - **Company:** KION GROUP AG - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Salary:** $134,250.0 - $179,000.0 - **Contract:** Permanent contract - **Skills:** Abstraction Layers, Artificial Intelligence, Airflow, Automation of Tests, BigQuery, Cloud Database, Continuous Integration, Data Architecture, Data Governance, Data Infrastructure, Data Systems, Dimensional Modeling, Distributed Systems, Python (Programming Language), Meta-Data Management, Metadata Standards, Open Source Technology, DataOps, SQL Databases, Data Streaming, Enterprise Data Management, Generative AI, Data Layers, Kubernetes, Data Lineage, Deployment Automation, Data Pipelines, Docker, Microservices - **Published:** July 27, 2026 - **Apply:** https://kiongroup.wd3.myworkdayjobs.com/KIONGroup/job/Atlanta-GA-United-States/Principal-Data-Architect_JR-0085724-1 ## About the Role We are seeking a highly experienced Semantic Context Data & AI Engineer to help evolve our Enterprise Data Platform into an AI-native, context-aware ecosystem. This role focuses on engineering semantic layers, certified KPI frameworks, metadata systems, and AI-ready data abstractions that enable trusted analytics and reliable AI consumption. This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines strong technical execution skills with architectural thinking and cross-functional collaboration., * 10-15+ years of experience in enterprise-scale cloud data engineering and distributed systems. * Strong hands-on experience building modern data platforms in GCP (BigQuery, Dataform, Pub/Sub, Composer/Airflow, Cloud Run). * Deep expertise in SQL, Python, and data modeling (dimensional modeling, lakehouse architectures). * Experience with open-source tech stack such as Iceberg, Trino, Kubernetes, Docker etc. * Hands-on experience with metadata management, lineage tracking, observability, and data quality frameworks. * Experience building both batch and real-time streaming data systems. * Strong understanding of semantic modeling, business metric governance, and AI consumption patterns. What Will Set You Apart : * Experience with Data Mesh or domain-driven data architecture. * Exposure to AI/LLM integration patterns including Retrieval-Augmented Generation (RAG). * Supply chain background Location & Authorization: This is a hybrid role requiring proximity to one of our U.S. offices (Atlanta GA, Grand Rapids MI, Milwaukee WI). Applicants must be authorized to work in the U.S. without the need for current or future sponsorship. ## Description * Design and implement enterprise semantic models and certified KPI layers to ensure trusted, reusable business metrics. * Build AI-safe data abstraction layers that prevent metric recomputation and ensure consistency across analytics and AI use cases. * Develop and enforce data contracts, metadata standards, and semantic governance frameworks across domains. * Engineer scalable batch and real-time streaming data pipelines in a modern cloud environment (GCP preferred). * Collaborate with AI/ML teams to design reliable grounding strategies for AI applications and agents. * Implement metadata management capabilities including cataloging, lineage, observability, and automated data quality checks. * Apply DataOps principles including CI/CD, automated testing, and deployment automation for data products. * Support domain-oriented and microservices-based data architecture patterns. * Mentor engineers and promote best practices in semantic modeling, governance, and AI-ready platform design. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [New AI-Centric SDLC: Rethinking Software Development with Knowledge Graphs](https://www.wearedevelopers.com/videos/1417-new-ai-centric-sdlc-rethinking-software-development-with-knowledge-graphs) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)