Neuroscience Data Platform Engineer

Stanfordlivetickets; Palo Alto (CA); On-site; USD 130,000 - 180,000; Full time

Job summary

Enigma's Tolias Lab in Palo Alto seeks a Data Engineer to build and maintain the data platform supporting Enigma’s experimental workflows. You will design, implement, and operate ETL/ELT pipelines moving raw data from acquisition to downstream stores, with emphasis on reliability, scalability, and observability.

You will partner with scientists and engineers to create interfaces, schemas, and tooling, productionize research code, and optimize data movement across storage, compute, and network

Qualifications

• Experience with Python and SQL in data engineering roles.
• Experience designing and operating production ETL/ELT pipelines.
• Experience with relational and NoSQL databases and object storage.
• Strong ability to design durable data models and schemas.

Responsibilities

• Design, build, and operate ETL/ELT pipelines connecting acquisition software, processing code, metadata registries, storage, and databases.
• Own the data layer end-to-end including schema design, migrations, backups, and durable models.
• Improve reliability, scalability, observability, and performance across the data stack; lead incident response.
• Build tooling to help researchers inspect datasets and diagnose or rerun jobs.
• Productionize research code: testing, packaging, deployment, monitoring, documentation.

Skills

Python

SQL

Data modeling

ETL/ELT

Data governance

Docker

Kubernetes

APIs

Airflow/Dagster/Prefect

Collaborative research

Tools

Relational DBs

NoSQL

Object storage

Airflow

Dagster/Prefect

Docker

Kubernetes

Job description

Enigma's Tolias Lab in Palo Alto seeks a Data Engineer to build and maintain the data platform supporting Enigma’s experimental workflows. You will design, implement, and operate ETL/ELT pipelines moving raw data from acquisition to downstream stores, with emphasis on reliability, scalability, and observability.

You will partner with scientists and engineers to create interfaces, schemas, and tooling, productionize research code, and optimize data movement across storage, compute, and network