Sr Data Engineering Manager

Imperative Care•Campbell, CA

4d•$220,000 - $245,000•Onsite

About The Position

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.

Requirements

Bachelor’s degree in computer science, software engineering, data engineering, or a related discipline and a minimum of 12 years of experience in data engineering / architecture, applied AI / ML, or enterprise application development; or an equivalent combination of education and related experience.
Demonstrated experience architecting modern data platforms—semantic layer and knowledge graph, and / or medallion / lakehouse—and integrating heterogeneous enterprise source systems and unstructured content.
Experience should include a minimum of 2 years of progressive recent, hands-on experience building production generative-AI and agentic solutions (LLMs, agent frameworks, RAG, orchestration, tool / function calling).
Knowledge of data governance and security models, including RBAC / ABAC, and of bi-directional data sync and data-cleansing patterns.
Proven ability to deliver pilots and proofs-of-concept end-to-end and scale them into production.
Cloud infrastructure hands-on knowledge; experience managing vendors / MSPs (SOWs, licensing, budgets) preferred.
Effective communicator across technical and business audiences.

Nice To Haves

Master’s degree preferred.
Experience in or alongside regulated environments—GxP, 21 CFR Part 11, computer system validation (CSV / CSA), GAMP 5, and HIPAA—strongly preferred.

Responsibilities

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, HubSpot, 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 agent—driving adoption, educating stakeholders, and promoting a culture of experimentation and data-driven decision-making.
Translate fluently between technical and non-technical audiences and drive alignment across IT, Finance, Sales, Marketing, Legal, and Quality.
Contribute to hiring, onboarding, and mentoring as the capability scales.
Ensure solutions meet compliance and security expectations and are designed for scalability from the outset.