Data Engineer

Job Description

The Tolias Lab (https://toliaslab.org/) in the Department of Ophthalmology (http://med.stanford.edu/ophthalmology.html) in the School of Medicine is seeking a Data Engineer (Software Developer 2) to build and maintain the data platform supporting Enigma’s experimental workflows. You will partner with scientists and engineers to turn evolving experimental requirements into clear interfaces, observable pipelines, and dependable tools. You will develop reliable systems that move raw data from data acquisition systems through ETL/ELT transformation into downstream data and metadata stores. This role will involve data lakehouse and schema design and administration, data governance and observability, and performance optimization.

The ideal candidate enjoys owning systems end to end while working in a highly collaborative research environment.

About Enigma

Enigma is building the experimental and computational infrastructure needed to understand intelligence through large-scale neuroscience. Our teams work across experimental development, neurophysiology, data engineering, and machine learning to turn complex biological data into reliable, accessible scientific resources.

Within Enigma, the Experimental Development (xDev) team designs and supports neurophysiology and behavioral experiments. xDev develops the systems that connect experimental hardware and acquisition software with data processing and downstream analysis—working closely with our data infrastructure engineering and our scientific data analysis and modeling teams.

Responsibilities:

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 that hold up over years of scientific work

Improve reliability, scalability, observability, and performance across the data stack, and lead troubleshooting/incident response when things break

Build tools with and to help researchers and our in-house AI agents discover datasets, inspect processing state, and safely diagnose or rerun failed jobs

Productionize research code — testing, packaging, deployment, monitoring, documentation

Partner with data acquisition, infrastructure engineering, scientific analysis and AI modeling teams.

Preferred Qualifications:

Strong proficiency in Python and SQL

Experience with relational and NoSQL databases, as well as object storage (e.g., S3, MinIO, Ceph)

Experience designing, building, and operating production ETL/ELT pipelines

Experience designing durable data models and schemas for complex, evolving datasets

Experience building high-throughput ingestion systems and optimizing data movement across storage, compute, and network boundaries

Experience with Docker and container orchestration platforms such as Kubernetes

Strong communication skills and experience working in cross-functional teams

Experience designing APIs or service interfaces used by multiple teams

Experience with scientific computing and large-scale neuroscience or other scientific data

Workflow orchestration experience with Airflow, Dagster, Prefect, or similar tools

Experience in building pipelines with and for autonomous, agentic AI systems

Experience with performance-critical compiled or systems languages (C, C++, or Rust)

Familiarity with data provenance and lineage tracking, metadata catalogs, or dataset versioning

As part of your application, please submit the following

two separate attachments

:

Cover Letter -

Please provide a cover letter describing your interest in joining our lab and the position.

Technical Experience Responses -

In a separate document, please answer each of the questions below in a few sentences. Please upload your responses as a single, separate attachment to your application.

Please describe your experience working with SQL and NoSQL databases.

Please describe you experience operating ETL/ELT data pipelines.

Please describe your experience with orchestration tooling such as Kubernetes and Ray.

You’ll Thrive in This Role If You:

Are excited by a fast-paced, production-focused research environment that often requires switching between many hats

Can move quickly without sacrificing rigor

Care about making complex workflows reliable and easy to use

Enjoy translating ambiguous research needs into pragmatic technical systems

Take ownership across design, implementation, deployment, and operations

Enjoy collaborative design and working closely across team boundaries<