Data Engineer, Knowledge Graphs

Mithrl•San Francisco, CA

2d•Onsite

About The Position

We are hiring a Data Engineer, Knowledge Graphs to build the infrastructure that powers Mithrl’s biological knowledge layer. You will partner closely with the Data Scientist, Knowledge Graphs to take curated knowledge sources and transform them into scalable, reliable, production ready systems that serve the entire platform. Your work includes building ETL pipelines for large biological datasets, designing schemas and storage models for graph structured data, and creating the API surfaces that allow ML engineers, application teams, and the AI Co-Scientist to query and use the knowledge graph efficiently. You will also own the reliability, performance, and versioning of knowledge graph infrastructure across releases. This role is the bridge between biological knowledge ingestion and the high performance engineering systems that use it. If you enjoy working on data modeling, schema design, graph storage, ETL, and scalable infrastructure, this is an opportunity to have deep impact on the intelligence layer of Mithrl.

Requirements

Strong experience as a data engineer or backend engineer working with data intensive systems
Experience building ETL or ELT pipelines for large structured or semi structured datasets
Strong understanding of database design, schema modeling, and data architecture
Experience with graph data models or willingness to learn graph storage concepts
Proficiency in Python or similar languages for data engineering
Experience designing and maintaining APIs for data access
Understanding of versioning, provenance, validation, and reproducibility in data systems
Experience with cloud infrastructure and modern data stack tools
Strong communication skills and ability to work closely with scientific and engineering teams

Nice To Haves

Experience with graph databases or graph query languages
Experience with biological or chemical data sources
Familiarity with ontologies, controlled vocabularies, and metadata standards
Experience with data warehousing and analytical storage formats
Previous work in a tech bio company or scientific platform environment

Responsibilities

Build and maintain ETL pipelines for large public biological datasets and curated knowledge sources
Design, implement, and evolve schemas and storage models for graph structured biological data
Create efficient APIs and query surfaces that allow internal teams and AI systems to retrieve nodes, relationships, pathways, annotations, and graph analytics
Partner closely with the Data Scientists to operationalize curated relationships, harmonized variable IDs, metadata standards, and ontology mappings
Build data models that support multi tenant access, versioning, and reproducibility across releases
Implement scalable storage and indexing strategies for high volume graph data
Maintain data quality, validate data integrity, and build monitoring around ingestion and usage
Work with ML engineers and application teams to ensure the knowledge graph infrastructure supports downstream reasoning, analysis, and discovery applications
Support data warehousing, documentation, and API reliability
Ensure performance, reliability, and uptime for knowledge graph services