Data Engineer (Software Developer 2)
- Compensation
- Not disclosed
- Location
- Stanford, CA, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Nice to have
- 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
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