Senior AI Context Engineer
Dematic · Wauwatosa, WI · 1 wk ago
Engineering$134k–$179k/yrFull-time
Tasks and Qualifications
- 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.
- 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).
- 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.
Pay Transparency: 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.