Full-Time
Manager – Enterprise Information Mgmt Platform Admin
Stanford Health Care
10,001+ employees
Compensation Overview
$83.98 - $111.27/hr
Palo Alto, CA, USA
In Person
 Engineering Management (2) |
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- BS/BA degree in information technology, information systems, business management, business analytics, business administration or a directly related field from an accredited college or university required.
- EIGHT (8) OR MORE YEARS EXPERIENCE AS A CLOUD ENGINEER, DATA ENGINEER, OR IN SIMILAR TECHNOLOGY ROLES WITH A RECENT FOCUS ON CLOUD TECHNOLOGIES REQUIRED.
- Certifications in Azure (e.g., Azure Solutions Architect, Azure Data Engineer) and/or Databricks required.
- Thought Leadership in Cloud Technologies: Recognized expert in cloud data engineering with a focus on innovative solutions and best practices.
- Comprehensive Knowledge of Azure Services: Mastery of Azure services, including the latest advancements and features.
- Strategic Vision for Data Architecture: Ability to define and drive the strategic vision for data architecture and engineering practices.
- Expertise in Infrastructure-as-Code and Automation: Mastery of infrastructure-as-code tools and automation strategies for cloud resource management.
- Innovative Solution Design: Proven track record of designing and implementing cutting-edge, scalable data solutions.
- Influential Leadership and Mentorship: Ability to lead cross-functional teams and mentor engineers at all levels, fostering a culture of innovation and excellence.
- Expertise in Data Security and Compliance: Authority on data security best practices and compliance regulations, influencing organizational policies.
- Exceptional Problem-Solving and Analytical Skills: Ability to tackle the most complex data challenges and provide strategic solutions.
- Strong Communication and Advocacy Skills: Exceptional ability to communicate technical concepts to executive leadership and external stakeholders, advocating for data initiatives.
- Contribution to the Data Engineering Community: Active participation in industry forums, conferences, and publications, contributing to the broader data engineering community.
- Makes reliable operational internal decisions independently and reliable tactical external decisions independently.
- Technical Leadership: Serve as the highest technical authority for a specific operational domain and/or technology. Provide technical leadership to the team and others within SHC on best practice uses and capabilities and how to utilize these to enable solutions.
- Innovation and Research: Drive innovation by researching and implementing cutting-edge technologies and methodologies.
- Develop vendor/platform relationships and influence product adoption based on SHC needs and priorities.
- Strategy & Architecture Design: Design complex data architectures and frameworks to support business needs. Define and maintain components of the Lakehouse reference architecture and standards. Collaborate with leadership on architectural blueprint, roadmap, and budgeting to support the EIM strategy.
- EIM Environment & Infrastructure Mgmt: Collaborate with Cloud Infrastructure leadership to align and optimize common operational processes (IaC, DevOps, FinOps, etc.).
- Design and enforce platform standards: environment separation (dev/test/prod), workspace segmentation, cluster policy design, catalog structure, secure library/dependency management, etc.
- Data Operations Management: Own domains and/or components of the data platform and operational processes.
- Analyze incidents and platform performance metrics to drive incident response excellence.
- Data Pipelines/Ingestion: Design and implement scalable and efficient data pipelines for various data types (structured, semi-structured, and unstructured) from various sources, including databases, APIs, and third-party services.
- Security and Compliance: Contribute to risk management with Security and Compliance to define potential solutions (i.e., threat modeling, control design and testing, evidence collection for audits, and continuous compliance automation) and new technology.
- Assess controls to meet healthcare data security requirements. Conduct recuring access audits/reviews.
- FinOps: Contribute to cost governance, usage showback/chargeback reporting, chargeback agreements, etc.
- Documentation and Training: Create comprehensive documentation of solutions and operations processes. Conduct training sessions for internal teams on operational protocols and best practices.
- Collaboration: Work closely with cross-functional teams and lead discussions on platform capabilities and operational processes/standards.
- Innovation: Evaluate and recommend new tools and technologies to enhance the cloud data platform's capabilities. Apply Systems Thinking to designs and solutions.
Company Size
10,001+
Company Stage
N/A
Total Funding
N/A
Headquarters
Palo Alto, California
Founded
2012
Simplify's Take
What believers are saying
- Alameda Health System partnership expands specialized care access for 400,000 Alameda residents.
- Retrieval Augmented Generation ensures accurate, hallucination-free clinical responses.
- EHR training modernization cuts onboarding time 50% and boosts clinician retention.
What critics are saying
- Epic's AI-native EHR upgrades outperform ChatEHR, eroding Stanford's advantage in 12-18 months.
- St. Rose Hospital collaboration taints Stanford's brand with safety-net operational liabilities.
- California Public Health halts ChatEHR expansion after FURM bias audit failures in 18-24 months.
What makes Stanford Health Care unique
- ChatEHR embeds generative AI directly into Epic EHR for 40-70% faster workflows.
- MedHELM provides continuous real-time evaluation of AI model safety and accuracy.
- DAX Copilot AI scribe automates notes, enabling clinician eye contact with patients.
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Your Connections
People at Stanford Health Care who can refer or advise you
Benefits
Health Insurance
Paid Vacation
401(k) Retirement Plan
Flexible Work Hours
Remote Work Options
Wellness Program
Mental Health Support
Company News
LogicSource, a procurement services and technology provider, has been named to The Boston Globe and Statista's New England's Fastest-Growing Companies 2026 list. The company achieved 104% revenue growth during the recognition period whilst expanding its workforce by 20%. Based in Westport, Connecticut, LogicSource completed 40,000 sourcing events annually for clients using its OneMarket technology platform, which now holds over $200 billion in cross-industry spend benchmarks. The company focuses on indirect procurement across categories including marketing, logistics, packaging and facilities. LogicSource serves clients such as lululemon, Tractor Supply Co., and Stanford Healthcare. The ranking, published by The Boston Globe in partnership with Statista, evaluates businesses based on sustained revenue growth and operational scale.
Stanford Health Care took the stage at HIMSS to share their EHR training success. Amplifire + Kahuna Partnership were honored to see this work come to life during Stanford Health Care's presentation at HIMSS Global Health Conference & Exhibition last week. Anne Hyland, Vice President of EHR Learning, and Michael Walker, Senior Director of New Business at Amplifire, were able to hear first-hand how their team handled the challenges and the measurable outcomes they've achieved. It was a powerful reminder of what's possible when innovation is paired with a clear focus on outcomes. What stands out most is Stanford Health Care's thoughtful approach to modernizing onboarding. Rather than accepting traditional, one-size-fits-all training, they've embraced a more personalized, data-driven model that meets clinicians where they are, respecting prior experience while ensuring mastery of critical workflows. The result is not only greater efficiency, but a more confident, prepared workforce ready to deliver high-quality care from day one. As highlighted in their broader work, this approach has also enabled earlier identification of struggling learners and more targeted support, ultimately strengthening both performance and satisfaction. Stanford Health Care continues to set the standard for innovation in workforce development, and their recent feature in Healthcare IT News highlights just how impactful that work has been. By reimagining EHR training, their team has successfully reduced training time by 50% while simultaneously improving learning retention, an achievement that speaks to both their strategic vision and deep commitment to clinician success. Congratulations to the entire Stanford Health Care team on this well-deserved recognition. Their leadership is not only advancing their own organization, but helping to shape the future of healthcare workforce development across the industry.
Nikesh Kotecha on blueprint for trust: how ChatEHR establishes a framework for Responsible AI in clinical care. Stanford Health Care, part of Stanford Medicine, was awarded a 2025 InterSystems Impact Award for its AXIOM initiative for fast access to electronic health records. In turn, AXIOM underpins the ChatEHR medical chatbot that Nikesh Kotecha, head of the Data Science team at Stanford Health Care, presents below. The Impact Award recognizes the work of the team to deliver low-latency, comprehensive access to patient data from complex electronic health record (EHR) systems. This foundation has been essential to enabling the ChatEHR platform. Contribution by Nikesh Kotecha The integration of Large Language Models (LLMs) into health systems holds the promise of reducing administrative burden and cognitive overload for clinical teams. However, deploying such tools in clinical workflows requires a commitment to safety, governance, and continuous monitoring. Stanford Medicine's ChatEHR platform, an initiative developed as part of an enterprise investment in establishing a Data Science pillar, serves as an essential case study for any health system aiming for responsible AI adoption. Setting up a responsible clinical AI system requires focus on three key areas: a robust platform architecture, transparent governance, and continuous evaluation. A significant barrier to integrating AI tools into clinical workflows is often fragmented data sources and latency in data access. Traditional reporting systems were unsuitable for the real-time applications needed at the point of care. The ChatEHR Platform tackles this with a sophisticated, four-pillar architecture: * LLM Router: Provides secure access to a variety of models, standardizing calls and handling centralized logging. * Real-Time Data Access: Fetches and organizes clinical information using Fast Healthcare Interoperability Resources (FHIR). This enables near real-time responsiveness by combining FHIR and HL7v2 messaging with optimized query performance. * Function Server: Transforms generic AI capabilities into healthcare-specific functions and task-specific endpoints, powering workflow automations. * EHR Integration: Manages secure connections and integrates the custom UI directly into Epic Hyperspace, maintaining authentication and patient context. This scalable architecture, underpinned by Stanford's AXIOM framework, proved critical for performance. For instance, the system cut retrieval times by over 95% - from nearly two minutes to four seconds - for some AI workflows. This focus on low-latency data access is fundamental to clinical decision-making and the AXIOM initiative, which provides this breakthrough data capability, was recognized as an InterSystems Impact Award winner in 2025. Responsible AI deployment starts with organizational frameworks and strong governance models. ChatEHR is explicitly guided by Stanford Medicine's Responsible AI Lifecycle. Oversight is managed through the Data Science Executive Committee (DSEC), ensuring transparency and alignment throughout the project life cycle. Furthermore, all system operations adhere to HIPAA and HITECH standards, utilizing role-based access controls inherited directly from the EHR to limit data exposure. This structured approach ensures that AI deployment is treated not as a one-off technical build, but as a governed, enterprise-wide strategy. Crucial to maintaining trust and safety is developing approaches for evaluating LLM performance, a task made challenging by the flexibility and variability of generative AI outputs.ChatEHR implemented MedHELM (Medical Holistic Evaluation of Language Models), a generative AI evaluation framework developed in collaboration with the Stanford Center for Biomedical Informatics Research (BMIR) and the Institute for Human-Centered AI (HAI) By combining governance, an embedded platform for low-latency data access, and monitoring via the MedHELM framework, the ChatEHR initiative provides a replicable blueprint for health systems seeking to translate AI innovation into measurable clinical and operational impact while upholding standards of safety and responsibility. Nikesh Kotecha, Head of Data Science at Stanford Health Care, tasked with building a group to develop and deploy AI-guided models into clinical and operational workflows. Prior to that, Nikesh started and led the informatics efforts at the Parker Institute for Cancer Immunotherapy, a non-profit organization bringing together 7 top cancer centers Organization: Stanford Health Care Stanford Health Care's AXIOM initiative (Advance Extraction for Intelligent Orchestration and Medical Insight) is a transformative solution that enables fast access to complex electronic health record (EHR) data. Using a unique recursive data retrieval method, AXIOM minimizes duplication and reduces query times from minutes or hours to seconds. It leverages a FHIR (Fast Health Interoperability Resources) repository on InterSystems IRIS, to power advanced AI and large language model (LLM) applications. This architecture enables real-time clinical insights, enhances care team collaboration, and supports use cases such as augmented triage and optimized patient flow across a variety of care settings. Sponsored by InterSystems and selected by an independent panel of judges from Massachusetts Institute of Technology (MIT).
Stanford section chief of preventive cardiology to receive the 2025 Joseph A. Vita Award.
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