Senior Data Engineering Manager

Imperative Care; Campbell (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Stock options

401k

Health benefits

Team events, competitions & activities

Job summary

Imperative Care seeks a Sr. Data Engineering Manager to build the semantic data platform and enterprise knowledge graph that underpins AI capabilities.

You will lead design, pilot, and production deployment of unified data platforms and AI agents that connect ERP, CRM, marketing, contracts, and BI, including governance, security, and data stewardship. You will work across IT, Finance, Sales, Marketing, Legal, and Quality to scale agent development and foster data-driven decision making, with a

Qualifications

• 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)
• Demonstrated experience architecting modern data platforms—semantic layer and knowledge graph, and / or medallion / lakehouse
• Knowledge of data governance and security models (RBAC / ABAC), and data‑cleansing patterns
• Experience in regulated environments (GxP, 21 CFR Part 11, CSV / CSA, GAMP 5, HIPAA) preferred
• Cloud infrastructure hands‑on knowledge; experience managing vendors / MSPs (SOWs, licensing, budgets) preferred
• Effective communicator across technical and business audiences

Responsibilities

• Build and lead Imperative Care's semantic data warehouse and knowledge graph strategy.
• Architect, build, pilot, and deploy a unified data platform and AI agents orchestration across core systems.
• Design and own a modern semantic data platform connecting ERP, CRM, marketing, contracts, purchasing, QM, and BI with unstructured content.
• Partner with MSPs and vendors to scale agent development and workflow automation.
• Enable business teams to self‑serve analytics and governance across data assets.
• Act as AI thought leader, promoting experimentation and data‑driven decision making.

Skills

Pilot delivery of proofs-of-concept

Generative AI / agentic solutions

Production AI systems

Semantic data platforms / knowledge-gn

RBAC/ABAC security

Data governance

Cross-functional communication

Vendor/MSP management

Cloud infrastructure hands-on

Education

Bachelor's degree in CS / related field

Master's degree preferred

Tools

LLMs

Agent frameworks

RAG orchestration

Knowledge-graph platforms

Job description

• 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

Requirements

• 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