Description
Under the direction of the Director of Analytics, the Data Enablement Manager plays a pivotal role in advancing UCLA Health's Data and AI strategy. This leader will accelerate our data to AI pipeline by overseeing the development of scalable data products, semantic layers, and AI driven workflows that power insight delivery across the health system.
In this role, you will lead a high impact team of data consultants and analytics professionals responsible for transforming complex health system data into actionable intelligence. You will guide the creation and maintenance of semantic models (e.g., Power BI), measurement frameworks("measurement as a product"), and AI products that enable faster, more reliable access to data in the right format.
A key focus of this position is supporting Agentic AI initiatives. Your team will operate under a Build-Operate-Transfer (BOT) model – rapidly developing, stabilizing, and transitioning AI enabled solutions that solve high value operational and clinical challenges at scale.
The Data Enablement Manager ensures alignment with UCLA Health's enterprise strategy and maximizes the value of data and AI across the organization. This is a mission critical leadership role for someone passionate about bridging the gap between data, AI, and real world workflow transformation in healthcare.
Flexible hybrid role allows for a blend of remote and on site work, requiring presence on site about 15% of the time, and as needed based on operational requirements. Travel to the "home office" location is not reimbursed. Each employee will complete a FlexWork Agreement with their manager to outline expectations and ensure mutual understanding. These arrangements are periodically reviewed and may be adjusted or terminated as necessary.
Salary offers are based on a variety of factors including qualifications, experience, and internal equity. The full salary range for this position is $128,500 - $298,100 annually. The University anticipates offering a salary between the minimum and midpoint of this range.
Qualifications
- Bachelor's Degree (in Computer Science, Engineering Discipline, or STEM Major preferred)
- Clarity Data Model/ Caboodle/ Cogito – Within 3 months of hire
- Python Certification (or Equivalent Coursework) – Within 3 months of hire
- 5+ Years – Minimum 5 years of experience managing data, analytics, and AI solutions in a healthcare environment
- 4+ Years – Experience leading and/or managing teams
- 1+ year – Machine Learning and Data Science Coursework, Certifications
- Ability to lead and grow a team of technically adept IT professionals who consult on technology solution options, understand business objectives, and drive impact through data and technology usage and implementations.
- Ability to explain complex technical concepts – such as how vector embeddings solve a search problem – to non technical clinical and business leaders to translate requirements, reduce blockers for projects, and support organizational objectives.
- Ability to lead the team through build, operate, and transfer phases of agentic solutions, requiring a keen eye for governance and understanding of how other departments take ownership of new, stabilized AI tools.
- Deep understanding of Large Language Model (LLM) orchestration, specifically how to build "Agents" that can reason, use tools, and execute workflows autonomously. Ability to foster and inculcate this understanding in team members to drive outcomes from these technologies.
- Ability to identify hires who possess the rare blend of Python/SQL engineering, healthcare knowledge, and consulting soft skills.
- Ability to stay up to date on changing technology landscape to advise and deliver the most optimal technical solution to solve problems across the enterprise.
- Knowledge of Retrieval-Augmented Generation (RAG) patterns, including how to manage embeddings, vector indexing, and the "chunking" of unstructured data for AI consumption. Ability to partner with data engineering and machine learning engineering to optimize these functions while driving business outcomes.
- Mastery of building complex, multi-functional semantic layers (e.g., Power BI Datasets, dbt models, or Fabric Semantic Links) that serve as a "Single Source of Truth" for both human analysts and AI agents.
- Knowledge of how to support a production grade AI product, focusing on data quality, feature stability, and performance monitoring.
- Ability to write, review, and optimize Python and SQL code (across platforms like Databricks) to support a team utilizing these technologies to manipulate large‑scale clinical and operational datasets.
- Ability to set clear KPIs for a team working on cutting edge AI projects where the "correct" path isn't always documented.
- Ability to conduct high level code reviews and provide technical guidance on complex data transformation and AI embedding logic.
- Ability to create an environment where expert