UCLA Health is seeking a Senior Data Engineer to join our Data Architecture team and help build the next generation of enterprise data platforms that power analytics, machine learning, artificial intelligence, and data-driven decision-making across one of the nation's leading academic health systems.

In this highly visible role, you will design and develop scalable data solutions that enable enterprise reporting, advanced analytics, machine learning, generative AI, and operational intelligence. You'll work at the forefront of cloud and data modernization initiatives, building robust data pipelines, reusable engineering frameworks, curated data products, and foundational platform capabilities that support critical healthcare operations and innovation.

This opportunity is ideal for an experienced engineer who enjoys solving complex data challenges, designing scalable architectures, and delivering high-quality data products that create meaningful impact for patients, providers, researchers, and business leaders.

What You'll Do

As a Senior Data Engineer, you will:

• Design, develop, and maintain scalable data pipelines that ingest, transform, validate, and deliver data from complex enterprise applications and external data sources.
• Build foundational data infrastructure that supports enterprise reporting, analytics, machine learning, data science, feature engineering, and AI-driven solutions.
• Develop trusted, well-governed data products, curated datasets, and reusable data models that are reliable, scalable, and easy to consume.
• Engineer solutions across diverse healthcare domains, including clinical, operational, financial, research, and administrative data.
• Implement modern data engineering best practices, including modular design, automated testing, CI/CD, monitoring, orchestration, observability, and production support.
• Create and maintain technical architecture documentation, data dictionaries, lineage artifacts, runbooks, and data quality frameworks.
• Collaborate with data architects, analysts, data scientists, application teams, product managers, and business stakeholders to deliver scalable data solutions aligned with organizational priorities.
• Support enterprise cloud modernization initiatives involving Azure-based data services, Databricks, data lakehouse architectures, enterprise data warehousing, ML-ready datasets, and feature stores.
• Optimize the performance, reliability, scalability, and usability of enterprise data pipelines and analytical assets.
• Establish and promote standards for data modeling, metadata management, naming conventions, security, governance, data quality, and reusable engineering frameworks.
• Lead or contribute to strategic initiatives by defining technical architectures, implementation patterns, delivery approaches, and operational support models.
• Participate in cross-functional architecture reviews, solution design discussions, and technology planning efforts.
• Mentor team members through code reviews, technical guidance, documentation, workshops, and knowledge-sharing sessions.
• Partner with leadership to advance engineering standards, platform capabilities, and the long-term data architecture roadmap.

Why UCLA Health?

At UCLA Health, you'll have the opportunity to work with modern cloud technologies, advanced analytics platforms, and emerging AI capabilities while helping improve healthcare outcomes for millions of patients. You'll collaborate with passionate professionals across technology, research, and clinical domains to build innovative solutions that support one of the most respected healthcare organizations in the country.

Join us and help shape the future of healthcare data, analytics, and AI.

Salary range: $109,900 - $243,900
Salary offers are based on a variety of factors including qualifications, experience, and internal equity The University anticipates offering a salary below the midpoint of this range.

• 5+ years of experience in data engineering, software engineering, backend engineering, analytics engineering, or related technical roles.
• Strong programming experience with Python, including the ability to write clean, modular, maintainable, and production-quality code.
• Strong SQL skills and hands-on experience working with relational databases such as SQL Server, Oracle, PostgreSQL, or similar platforms.
• Solid understanding of data warehousing, ETL/ELT design patterns, dimensional modeling, data lake/lakehouse concepts, and large-scale data processing.
• Experience designing and building data pipelines that ingest, transform, validate, and publish data from multiple source systems.
• Experience working with modern data platforms such as Databricks, Spark, Delta Lake, Snowflake, Microsoft Fabric, Synapse, or similar technologies.
• Working knowledge of cloud platforms such as Azure, AWS, or GCP. Microsoft Azure experience is valuable.
• Experience with orchestration and pipeline execution tools such as Azure Data Factory, Databricks Workflows, Airflow, or similar platforms.
• Experience with production engineering practices such as Git-based development, CI/CD, automated testing, code reviews, logging, monitoring, and release management.
• Ability to troubleshoot complex data quality, pipeline performance, orchestration, and reliability issues.
• Experience implementing data quality checks, reconciliation processes, observability patterns, metadata capture, and operational monitoring.
• Familiarity with machine learning data workflows, feature engineering, feature stores, ML-ready datasets, MLflow, MLOps, or AI/GenAI-supporting data infrastructure.
• Understanding of data governance concepts, including metadata management, lineage, access controls, data stewardship, documentation standards, and secure handling of sensitive data.
• Ability to collaborate effectively with technical and non-technical stakeholders, including architects, analysts, data scientists, product managers, business teams, and application owners.
• Strong written and verbal communication skills, with the ability to explain technical decisions clearly and document solutions for long-term maintainability.
• Bachelor’s degree in Computer Science, Computer Engineering, Data Engineering, Information Systems, or a related technical field.
• Healthcare, health sciences, clinical data, Epic, research data, or regulated data environment experience is valuable, but not required.