Data Platform Engineer — ETL/ELT for Neuro AI

Stanford University; Palo Alto (CA); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Competitive salary

Mentoring and career development

Job summary

Stanford University’s Tolias Lab in Ophthalmology seeks a Data Engineer to design, build, and operate end-to-end data pipelines from acquisition through ETL/ELT, enabling research workflows and scalable data platforms. You will partner with scientists and engineers to implement interfaces, observability, and dependable tools, ensuring robust data movement and governance across the lakehouse stack.

The role emphasizes productionizing research code, building durable data models, and collaborating

Qualifications

• Design, build, and operate ETL/ELT and high-throughput ingestion pipelines connecting acquisition software, processing code, metadata registries, object storage, and databases
• Own the data layer end-to-end: schema design, indexing, query optimization, migrations, backups, and durable data models

Responsibilities

• Design, build, and operate ETL/ELT pipelines
• Own data layer end-to-end: schema design and backups
• Improve reliability, scalability, observability, and performance across the data stack
• Build tools to help researchers and AI agents discover datasets and inspect processing state
• Productionize research code — testing, packaging, deployment, monitoring, documentation
• Partner with data acquisition, infrastructure engineering, and AI modeling teams

Skills

Python

SQL

Docker

Kubernetes

ETL pipelines

Data modeling

APIs

Airflow

Education

Bachelor's degree in CS or related

Tools

PostgreSQL

NoSQL

S3

MinIO

Job description

Stanford University’s Tolias Lab in Ophthalmology seeks a Data Engineer to design, build, and operate end-to-end data pipelines from acquisition through ETL/ELT, enabling research workflows and scalable data platforms. You will partner with scientists and engineers to implement interfaces, observability, and dependable tools, ensuring robust data movement and governance across the lakehouse stack.

The role emphasizes productionizing research code, building durable data models, and collaborating