Amazon Bioinformatics Jobs (NOW HIRING)

Job Summary:
Eli Lilly and Company is a global healthcare leader headquartered in Indianapolis, Indiana. They are seeking Data Architects to design and build the data infrastructure that enables AI-native drug discovery, creating schemas, ontologies, and data models to transform scientific data into actionable insights.
Responsibilities:
• Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data that serve discovery workflows across the portfolio.
• Define and maintain controlled vocabularies, metadata standards, and FAIR-compliant data frameworks in partnership with Preparedness4Insight.
• Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.
• Design and implement data lakehouse architecture using modern platforms (Databricks, Snowflake, or equivalent), including data storage patterns, partitioning strategies, and query optimization.
• Build and optimize ETL/ELT pipelines using Spark, dbt, or similar tools to transform raw scientific data into analytical and ML-ready formats.
• Implement real-time and streaming data integration (Kafka, Kinesis, event-driven patterns) connecting LIMS, instruments, and lab automation systems to the data infrastructure.
• Design and implement knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) that capture molecular, target, pathway, and experimental relationships across the discovery landscape.
• Architect specialized data solutions: array databases (TileDB) for genomics/imaging, document stores (MongoDB) for experimental records, and vector databases for embedding-based retrieval supporting ML and RAG workflows.
• Build query and traversal patterns that enable scientists and AI agents to ask relational questions across the entire data landscape.
• Partner with scientific software engineers to ensure data architectures are implementable, performant, and well-documented.
• Collaborate with Methods4Insight to design data structures that support analytical model training, deployment, and evaluation.
• Work with Tech@Lilly to define scaling strategies, ensure enterprise compliance, and transition data architectures to production-grade management.
• Contribute to build-versus-buy-versus-adopt decisions by evaluating commercial and open-source data platforms against Data Foundry requirements.
Qualifications:
Required:
• M.S. or PhD in Computer Science, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM field
• MS (with 6+ years) and PhD (with 2+ years) of data architecture, data engineering, or scientific informatics experience.
• Deep expertise in at least one of the focus areas: relational databases, data modeling and ontology engineering, data platform and lakehouse architecture (Databricks, Snowflake, Spark), or knowledge graph and specialized database systems (Neo4j, Neptune, MongoDB, TileDB)
Preferred:
• Working familiarity with multiple database paradigms — relational, graph, document, columnar, key-value — and strong SQL skills.
• Understanding of scientific data types and experimental workflows in life sciences or pharma (chemical, biological, HTE data).
• Strong communication skills with ability to translate data architecture concepts for both technical and scientific audiences.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data integration patterns.
• Pharmaceutical or biotech research industry experience, particularly in discovery data management or research informatics.
• Experience with semantic web technologies: RDF, OWL, SPARQL, Protégé, or equivalent ontology engineering tools.
• Hands-on experience with graph databases (Neo4j, Neptune, TigerGraph) and knowledge graph design patterns for scientific data.
• Data lakehouse architecture experience: Databricks (Delta Lake, Unity Catalog), Snowflake, or equivalent; ETL/ELT with Spark, dbt.
• Experience with streaming/real-time data platforms (Kafka, Kinesis, Flink) and event-driven architectures.
• Familiarity with LIMS, ELN systems (e.g., Benchling), and laboratory instrument data integration.
• Experience with vector databases (Pinecone, Weaviate, pgvector) and embedding-based retrieval for ML/RAG applications.
• Array database experience (TileDB, Zarr) for genomics, imaging, or high-dimensional scientific data.
• FAIR data principles implementation experience and Data Readiness Level frameworks.
• Scientific data standards and controlled vocabularies in chemistry (InChI, SMILES) or biology (Gene Ontology, UniProt).
• Experience with C, C++, or Rust for performance-critical data processing; familiarity with HPC data I/O patterns for large-scale scientific computations.
Company:
We're a medicine company turning science into healing to make life better for people around the world. Founded in 1876, the company is headquartered in Indianapolis, USA, with a team of 10001+ employees. The company is currently Late Stage.