Senior Data Engineer

UCLA HEALTH

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Description

We’re seeking an experienced Senior Data Engineer to join our team! In this role you will:

• Design and build scalable data pipelines and platforms that integrate complex enterprise and external data sources.
• Develop trusted data products, curated datasets, and reusable data models supporting reporting, analytics, data science, machine learning, and AI.
• Engineer solutions across clinical, operational, financial, research, and administrative healthcare data.
• Advance cloud-based data capabilities using Azure, Databricks, lakehouse architectures, enterprise data warehousing, and ML-ready data.
• Apply modern engineering practices, including automated testing, CI/CD, orchestration, monitoring, observability, and production support.
• Optimize data solutions for performance, scalability, reliability, security, and usability.
• Establish standards and reusable frameworks for data modeling, metadata, governance, data quality, security, and engineering.
• Develop technical architecture, data lineage, data dictionaries, runbooks, and other documentation supporting reliable enterprise data operations.
• Partner across technology and business teams to translate complex requirements into scalable, sustainable data solutions.
• Lead technical design and architecture discussions and help define implementation and operational support approaches for strategic initiatives.
• Mentor engineers through code reviews, technical guidance, workshops, and knowledge sharing.

Partner with leadership to shape engineering standards, platform capabilities, and the long-term data architecture roadmap.Salary Range: $108,900.00 – $241,500.00 annually

Salary offers are based on a variety of factors including qualifications, experience, and internal equity.  The University anticipates offering a salary between the minimum and midpoint of this range.

Qualifications

• 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.

• 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, or similar technologies.

• Working knowledge of cloud platforms such as Azure, AWS, or GCP. Microsoft Azure experience is especially 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.

• Experience with machine learning data workflows, feature engineering, feature stores, ML-ready datasets, MLflow, MLOps, or AI/GenAI-supporting data infrastructure including RAG based systems.

• 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. A master’s degree is a plus.