Advisor - Data Architect, Data Foundry

Data Architect

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We're looking for people who are determined to make life better for people around the world.

Location: San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN

Reports To: Lead, Data Architecture (R9), Architecture4Insight

Overview

Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.

Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation & Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities—serving both human scientists and autonomous AI agents.

Position Summary

We are seeking Data Architects at multiple levels to design and build the data infrastructure that makes AI-native drug discovery possible. You will create the schemas, ontologies, data models, knowledge graphs, and platform architectures that transform raw scientific data into machine-actionable, FAIR-compliant, insight-ready assets—serving both discovery scientists and autonomous AI agents.

This role is the foundation of Architecture4Insight. Everything the software engineering team builds—pipelines, APIs, prototypes—depends on the data models and platform architecture this team designs. You will work with deep knowledge of scientific data (chemical, biological, HTE, automation-generated) to create custom-fit solutions, then partner with View email address on click.appcast.io to scale and maintain them. The role spans three focus areas depending on expertise: data modeling & ontologies, data platform & lakehouse architecture, and knowledge graph & specialized data systems. You will independently design schemas, select technologies, and make build-vs-buy recommendations for their domain.

Responsibilities

Data Modeling & Ontologies

• 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.

Data Platform & Lakehouse Architecture

• 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.

Knowledge Graph & Specialized Data Systems

• 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.

Cross-Functional Partnership

• 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 View email address on click.appcast.io 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.

Basic Requirements

• 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 Qualifications

• 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.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women's Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.

Actual compensation will depend on a candidate's education, experience, skills, and geographic location.