Senior AI Data Engineer

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. We offer: Career Development Competitive Compensation and Benefits Pay Transparency Global Opportunities Learn More Here: Dematic provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. The base pay range for this role is estimated to be $134,250.00 - $179,000.00 at the time of posting. Final compensation will be determined by various factors such as work location, education, experience, knowledge and skills. Tasks and Qualifications: This is What You Will do in This Role: 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. What We are Looking For: 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. #LI-DP1