Senior Data Engineer

Join UCLA Health and help deliver trusted, scalable data capabilities for healthcare and enterprise needs. This Senior Data Engineer role focuses on designing production-ready data pipelines and lakehouse/warehouse solutions, building curated data products used across reporting, analytics, data science, machine learning, and AI, and leading technical standards that improve reliability, security, and usability. The position is based in Los Angeles, CA (onsite).

Responsibilities

• Design and build scalable data pipelines, platforms, and integrations for complex enterprise and external data sources.
• Create trusted data products, curated datasets, and reusable data models that support 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 production engineering practices such as automated testing, CI/CD, orchestration, monitoring, observability, and production support.
• Optimize 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 and supporting documentation including data lineage, data dictionaries, and runbooks for dependable enterprise operations.
• Partner with technology and business teams to translate complex requirements into scalable, sustainable data solutions.
• Lead technical design and architecture discussions, including implementation and operational support approaches for strategic initiatives.
• Mentor engineers through code reviews, technical guidance, workshops, and knowledge sharing.
• Collaborate with leadership on engineering standards, platform capabilities, and the long-term data architecture roadmap.

Requirements

• 5+ years of experience in data engineering, software engineering, backend engineering, analytics engineering, or related technical roles.
• Strong programming experience with Python, with the ability to write clean, modular, maintainable, production-quality code.
• Solid understanding of data warehousing, ETL/ELT design patterns, dimensional modeling, and data lake/lakehouse concepts, including large-scale data processing.
• Experience building pipelines that ingest, transform, validate, and publish data from multiple source systems.
• Experience with modern data platforms such as Databricks, Spark, Delta Lake, Snowflake, Microsoft Fabric, or similar technologies.
• Working knowledge of cloud platforms including Azure, AWS, or GCP, with Microsoft Azure experience 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 including Git-based development, CI/CD, automated testing, code reviews, logging, monitoring, and release management.
• Experience with machine learning data workflows including feature engineering, feature stores, ML-ready datasets, MLflow, MLOps, and AI/GenAI-supporting 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, including documenting technical decisions 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.

Compensation

• Salary Range: $108,900.00 - $241,500.00 annually
• Salary offers are based on qualifications, experience, and internal equity, with anticipation of offering a salary between the minimum and midpoint of the range.

Technologies

Python, Azure, AWS, GCP, Databricks, Spark, Delta Lake, Snowflake, Microsoft Fabric, Azure Data Factory, Databricks Workflows, Airflow, Git, CI/CD, MLflow, MLOps