Architect and build Imperative Care’s modern semantic data platform, knowledge graph, and enterprise AI foundation. Develop production generative AI agents, RAG solutions, intelligent workflows, and system integrations. Establish data governance, security, lineage, quality, and stewardship controls for a regulated environment. Enable self-service analytics and reporting while partnering with business, technology, vendors, and managed-service providers. Serve as an AI thought leader, drive adoption, and contribute to hiring and mentoring as capabilities expand.
Title: Sr Data Engineering Manager
This position is based in our Campbell, California offices. This position is on-site, full-time.
Why Imperative Care?
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.
What You’ll 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.
Modern Data Platform & Architecture
- 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.
What You’ll Bring:
- 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.
- 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.
- 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.
- 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.
Employee Benefits include a stake in our collective success with stock options, competitive salaries, a 401k plan, health benefits, generous PTO, and a parental leave program.
Join Us! Imperative Care
Salary Range: $220,000 – 245,000 annually
Please note that the salary information is a general guideline only. Imperative Care considers factors such as scope and responsibilities of the position, candidate's work experience, education/training, key skills, and internal equity, as well as location, market and business considerations when extending an offer. As part of our total rewards package, Imperative Care offers comprehensive benefits including a 401k plan, health benefits, generous PTO, a parental leave program and emotional health resources.
Imperative Care Campbell, California, USA Office
Dell Ave, Campbell, CA, United States, 95008
Similar Jobs
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Leads a team of 6–10 engineers building and operating cloud-native platform services. Owns hiring, coaching, delivery, roadmap, reliability, scalability, SLOs, on-call practices, and technical direction across hyperscaler, on-premises, and sovereign environments. Partners with senior engineering stakeholders, reviews designs and code, contributes to architecture and implementation, and communicates risks and trade-offs to leadership. Requires extensive production software experience, Kubernetes and distributed systems expertise, people management experience, hyperscaler knowledge, and familiarity with CI/CD, GitOps, and infrastructure as code.
Top Skills:
AWSAzureCi/CdContainersDistributed SystemsGitopsGoogle Cloud Platform (Gcp)Infrastructure As CodeKubernetes
Cloud • Computer Vision • Information Technology • Sales • Security • Cybersecurity
Lead and grow an engineering team to design, build, and drive adoption of an ontology-based semantic data modeling framework. Define architecture, tooling, governance, integrations with cloud-native data systems, and enablement programs. Partner with product, AI/ML, and security teams to translate domain complexity into reusable ontologies and measurable adoption metrics, mentoring an international team and fostering technical excellence.
Top Skills:
AIAmazon NeptuneApache FlinkSparkData LakeData LakehouseDistributed SystemsGraph DatabasesJanusgraphKafkaMetadata PlatformsNeo4JOwlRdfSchema RegistrySemantic LayersShaclSparql
Fintech • Payments • Financial Services
Lead and grow a Data Engineering team to build a scalable analytics platform. Set technical direction for dbt and Snowflake, raise analytics engineering standards, enable domain experts to contribute high-quality models, introduce AI-assisted workflows, and partner cross-functionally to ensure reliable, understandable enterprise data.
Top Skills:
AIDbtPythonSnowflakeSQL
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine



