Senior Data Engineer at UCLA Health

Job 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. • 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. As a condition of employment , the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer. Current/former UC employees are subject to a personnel file review.

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

Bachelor’s degree in computer science, Computer Engineering, Data Engineering, Information Systems, or a related technical field. A master’s degree is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaiazuremlci/cdpythonawsgcp

Platform: avature
Effort: medium