Senior AI Platform Engineer, Enterprise Data & DevOps

LE0010 Stanford Health Care; Palo Alto (CA); On-site; USD 140,000 - 185,000; Full time

Job summary

Stanford Health Care is seeking a Senior Enterprise Data Engineer to design complex data pipelines and curated data structures for use by data analysts. The role supports SHC’s enterprise data platform and collaborates with data scientists on DevOps, SecOps and FinOps processes.

You will lead in building automated IaC and monitoring capabilities, ensuring data quality and governance across multiple data domains, while guiding data engineers in design and standards adoption.

Qualifications

• BS/BA degree in information technology, information systems, business analytics, management or related field is required.
• 7+ years in reporting, analytics, BI, or healthcare tech OR 3+ years in cloud data lake technologies (Databricks) with DevOps/CI/CD.
• Advanced data modeling and relational database design knowledge.
• Experience deploying production AI/ML data solutions in cloud platforms (Azure, DataBricks).

Responsibilities

• Design and create complex data stores and pipelines for data analysts across SHC.
• Lead data movement processes to populate designed data structures across SHC.
• Tune performance and ensure data governance across data platforms and pipelines.
• Collaborate with data scientists on DevOps, SecOps and FinOps processes.

Skills

Relational database concepts

Data modeling

Cloud data platforms

PySpark

SQL

GitHub

DevOps concepts

Notebooks

Education

BS/BA degree in IT or related field

Tools

Azure

DataBricks

PySpark

Notebooks

GitHub

CI/CD

Job description

Stanford Health Care is seeking a Senior Enterprise Data Engineer to design complex data pipelines and curated data structures for use by data analysts. The role supports SHC’s enterprise data platform and collaborates with data scientists on DevOps, SecOps and FinOps processes.

You will lead in building automated IaC and monitoring capabilities, ensuring data quality and governance across multiple data domains, while guiding data engineers in design and standards adoption.