Edtech.com's Summary

Stanford University is hiring a Data Engineer (Software Developer 2) to build and maintain the data platform behind Enigma's neuroscience experiments in the Tolias Lab. The engineer builds reliable systems that move raw acquisition data through ETL/ELT transformation into downstream data and metadata stores. Day to day, the work pairs with scientists and engineers to turn changing experimental requirements into clear interfaces, observable pipelines, and dependable tools.

Highlights

Design, build, and operate ETL/ELT and high-throughput ingestion pipelines linking acquisition software, processing code, metadata registries, object storage, and databases.
Own the data layer end to end, including schema design, indexing, query optimization, migrations, and backups for long-lived scientific data models.
Improve reliability, scalability, observability, and performance across the data stack, and lead troubleshooting and incident response when things break.
Build tools that let researchers and in-house AI agents discover datasets, inspect processing state, and safely diagnose or rerun failed jobs.
Productionize research code through testing, packaging, deployment, monitoring, and documentation.
Partner with the data acquisition, infrastructure engineering, scientific analysis, and AI modeling teams.
Strong Python and SQL skills are preferred, along with experience in relational and NoSQL databases and object storage such as S3, MinIO, or Ceph.
Experience with Docker, Kubernetes, and workflow orchestration tools such as Airflow, Dagster, or Prefect is preferred.
Hold a bachelor's degree and five years of relevant experience, or a combination of education and relevant experience.
The posted salary range is $117,536 to $219,400 per year.

Data Engineer (Software Developer 2) Full Description

The Tolias Lab in the Department of Ophthalmology 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
Are energized by working as part of a cohesive team pursuing ambitious, long-term neuroscientific and AI goals

What We Offer:

A highly collaborative environment across neuroscience, engineering, and AI
Opportunity to contribute to a next-generation neurotechnology platform
Competitive salary and benefits
Strong mentoring and career development support

Responsibilities

Core Duties:

Conceptualize design, implement, and develop solutions for complex systems/programs.
Work with a variety of users to gain information, and develop intra-system tradeoffs between different users, as necessary; interact with a diverse client base and outside vendor contacts.
Document system builds and application configurations; maintain and update documentation as needed.
Provide technical analysis, design, development, conversion, and implementation work.
Work as a project leader, as needed, for projects of moderate complexity.
Serve as a technical resource for applications.
Compare, evaluate, and implement new features and technologies, and integrate them into the computing environment.
Follow team software development methodology.
Mentor junior software developers.

Minimum Education and Experience:

Bachelor's degree and five years of relevant experience, or a combination of education and relevant experience.

Knowledge, Skills and Abilities:

Expertise in designing, developing, testing, and deploying applications.
Proficiency with application design and data modeling.
Ability to define and solve logical problems for highly technical applications.
Effective communication skills with both technical and non-technical clients.
Ability to lead activities on structured team development projects.
Ability to select, adapt, and effectively use a variety of programming methods.
Knowledge of application domain.

Pay Range

$117,536-$219,400

Pay Frequency

Annually

Fixed Term

Stanford Schools and Units

School of Medicine

Full time or Part time

Full Time

Regular or Temporary

Regular

Job Category

Information Technology Srvcs

Posting Date

10/01/2026

Grade

Job Identification

201222

Notice to Applicants

The job duties listed are typical examples of work performed by positions in this job classifications and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned.

Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of their job.

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

Stanford University provides job pay ranges representing its good faith estimate of what the university reasonably expects to pay for a particular job. The specific pay offered to a selected candidate will be determined based on a wide range of factors that are unique to each candidate, including but not limited to geographic work location, relevant knowledge, skills and abilities, relevant education, years of relevant experience, depth and breadth of relevant experience, and performance; further including but not limited to other business and organization needs such as the scope and responsibilities of the position, the minimum qualifications, departmental budget availability, and market and internal equity across the university, school/ unit, department, as well as job reporting relationships.

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