Sr Data Engineering Manager

Title:

Sr Data Engineering Manager

This position is based in our Campbell, California offices. This position is on-site, full-time.

Do you want to make a real impact on patients? As part of our team at Imperative Care, you can help elevate care for patients suffering from stroke and other devastating vascular diseases. Every day, the technologies that we develop at Imperative Care directly impact people at the most vulnerable moments of their lives. Our focus is on the needs of the patient, and they come first in everything we do.

The Sr. Data Engineering Manager serves as the subject matter expert in this field to build and lead the foundation of Imperative Care’s modern data architecture and AI capability. This includes technology such as Modern Data Platform, Enterprise AI, Agentic AI, and Semantic Data Architecture. This role is an individual contributor responsible for leading and establishing Imperative Care's semantic data warehouse and enterprise knowledge graph strategy (AI-ready enterprise data foundation) and through collaboration efforts, spearhead the development of practical agentic AI capabilities across Imperative Care’s core business areas and systems.

This position designs and owns a modern, semantic data platform that unifies the company’s core business systems and unstructured content into a governed, connected data layer, and serves as the organization’s hands‑on builder and corporate leader for agentic AI. This role will architect, build, pilot, and deploy a unified data platform and drive AI agents’ orchestration directly across core business systems, enabling governed analytics, business enabled self‑service reporting, retrieval‑augmented generation (RAG), AI agents development, and workflow automation.

• Design and own a modern, semantic data platform that connects key business systems—ERP, CRM, marketing technologies, contract/legal management software, purchasing, quality management, and business intelligence (e.g., tools such as QAD, Salesforce, Hub Spot, Agiloft, Coupa, Propel, and Tableau)—together with unstructured and flat-file content, into a unified and governed data layer.
• Evaluate and select the target architecture, weighing a semantic / knowledge-graph approach that connects data largely in place against a medallion / star-schema warehouse, and define the roadmap, build-vs-virtualize decisions, and total cost of ownership.
• Build and maintain the knowledge graph and semantic layer using modern graph, semantic, and data‑integration tooling (e.g., tools such as knowledge-graph platforms and data-sync / virtualization tools), enabling bi‑directional sync, data cleansing, and master / reference data alignment.
• Implement enterprise data security and governance using role- and attribute-based access control (RBAC / ABAC), along with data lineage, quality, and stewardship controls appropriate to a regulated environment.

• Serve initially as the hands‑on builder—designing, prototyping, and deploying production AI agents and intelligent workflows that connect systems to actions and insights.
• Build agent capabilities including tool / function calling, context and memory management, multi‑agent orchestration, retrieval‑augmented generation (RAG), and human‑in‑the‑loop checkpoints.
• Implement prompt‑engineering and reasoning strategies, validate value through measurable outcomes, and iterate rapidly from pilot to production.
• Partner with managed‑service providers (MSPs) and vendors to scale agent development and workflow automation across business processes.

• Empower business teams to self‑serve analytics by providing the foundation of data, standards, and support that distributed report developers need.
• Enable the business to build their own report and support data governance efforts. Where needed, lead the buildout of selected high‑value reports directly.
• Collaborate with data and analytics teams to improve data reporting, forecasting, and decision‑support capabilities.

• Act as an AI thought leader and change…