Asks for

The ad says“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)”
The ad says“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)”
The ad says“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”
The ad says“Knowledge of data governance and security models, including RBAC / ABAC, and of bi-directional data sync and data-cleansing patterns”
The ad says“Cloud infrastructure hands-on knowledge; experience managing vendors / MSPs (SOWs, licensing, budgets) preferred”
The ad says“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”
The ad says“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”
The ad says“Knowledge of data governance and security models, including RBAC / ABAC, and of bi-directional data sync and data-cleansing patterns”
The ad says“Knowledge of data governance and security models, including RBAC / ABAC, and of bi-directional data sync and data-cleansing patterns”
The ad says“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”
The ad says“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)”
The ad says“Demonstrated experience architecting modern data platforms—semantic layer and knowledge graph, and / or medallion / lakehouse—and integrating heterogeneous enterprise source systems and unstructured content”
The ad says“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)”
The ad says“Demonstrated experience architecting modern data platforms—semantic layer and knowledge graph, and / or medallion / lakehouse—and integrating heterogeneous enterprise source systems and unstructured content”
The ad says“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)”
The ad says“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)”
The ad says“Knowledge of data governance and security models, including RBAC / ABAC, and of bi-directional data sync and data-cleansing patterns”
The ad says“Demonstrated experience architecting modern data platforms—semantic layer and knowledge graph, and / or medallion / lakehouse—and integrating heterogeneous enterprise source systems and unstructured content”
The ad says“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)”

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, 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

Benefits

Stock options
401k
Health benefits
Team events, competitions & activities

Preferred experience

Proven ability to deliver pilots and proofs-of-concept end-to-end and scale them into production
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
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)
Master’s degree preferred
Demonstrated experience architecting modern data platforms—semantic layer and knowledge graph, and / or medallion / lakehouse—and integrating heterogeneous enterprise source systems and unstructured content
Knowledge of data governance and security models, including RBAC / ABAC, and of bi-directional data sync and data-cleansing patterns
Experience in or alongside regulated environments—GxP, 21 CFR Part 11, computer system validation (CSV / CSA), GAMP 5, and HIPAA—strongly preferred
Cloud infrastructure hands-on knowledge; experience managing vendors / MSPs (SOWs, licensing, budgets) preferred
Effective communicator across technical and business audiences