Advisor - Scientific Data Engineer

Eli Lilly and Company•San Francisco, CA

About The Position

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. The Opportunity We are building something unprecedented — an AI foundation that will push the frontier on what is possible today across drug discovery research, from target identification and disease biology through translational science. The Applied Intelligence for Discovery (AI4D) team is a newly formed group within Lilly Research Laboratories that operates at the intersection of scientific delivery and core platform development. AI4D’s mission is connecting scientists to petabyte-scale data through natural language interfaces, automated analysis workflows, and intelligent search — and to convert early deployments into repeatable system standards and evaluation practices that scale across therapeutic areas. As a Scientific Data Engineer, you will close that gap. You will build the semantic layer, data harmonization infrastructure, AI-ready data products, and lakehouse architecture that bridge how data is stored and how AI systems need to consume it. You will be working at the intersection of the data infrastructure team and the generative AI engineers who build the systems scientists interact with.

Requirements

Bachelors degree in Computer Science, Data Engineering, Bioinformatics, or a related field + 8 years data engineering experience OR Masters degree and 5 years data engineering experience
Demonstrated expertise in building data pipelines, ETL/ELT workflows, and data products that serve downstream AI/ML systems
Strong SQL skills and experience with complex relational database schemas (hundreds of tables, multi-level joins, domain-specific conventions)
Experience with modern data platform technologies, including at least one of: Databricks, Snowflake, or equivalent lakehouse platforms
Experience with modern data engineering tools: dbt, Spark, Airflow, or similar orchestration and transformation frameworks
Proficiency in Python for data processing, scripting, and pipeline development
Experience with cloud data platforms (AWS preferred: Redshift, Athena, Glue, S3, or similar)
Familiarity with at least one of: vector databases, embedding pipelines, or semantic layer tooling
Strong communication skills — you can work effectively with both engineers who think in schemas and scientists who think in biology

Nice To Haves

Phd in data or related field
Experience with biomedical or scientific data: omics datasets (RNA-seq, proteomics, GWAS), clinical data, or laboratory information management systems
Experience in pharmaceutical, biotech, or life sciences environments
Familiarity with biomedical ontologies and controlled vocabularies (Gene Ontology, MeSH, ChEBI, HGNC) and their application to data integration
Experience building data products that serve AI/ML systems — feature stores, training datasets, evaluation benchmarks, or semantic annotations for text-to-SQL
Knowledge of data governance practices in regulated industries: data lineage, access controls, versioning, and auditability
Experience with knowledge graph technologies (Neo4j, Amazon Neptune, RDF/SPARQL) or graph-based data modeling
Deep experience with Databricks ecosystem: Unity Catalog for data governance, Delta Lake for ACID transactions, MLflow integration, and Databricks SQL for analytics workloads
Experience designing data architectures that bridge traditional bioinformatics workflows (Nextflow, R/Bioconductor) with modern lakehouse consumption patterns

Responsibilities

Data Harmonization and Lakehouse Architecture Design and build the data architecture that transforms raw and processed omics data into harmonized, AI-consumable layers
Build and optimize ETL/ELT pipelines that produce denormalized views, pre-computed aggregations, embedding-ready text representations, and feature stores optimized for AI system consumption
Implement data quality monitoring, automated profiling, and validation checks across harmonization layers
Create versioned, reproducible data snapshots that support model training, evaluation, and audit requirements in a regulated environment
Partner with the teams to extend harmonization patterns as data modalities expand beyond genomics and proteomics into spatial transcriptomics, perturbational data (Perturb-Seq), single-cell, and digital pathology
Semantic Layer and Schema Engineering Design and maintain a semantic layer over Lilly’s multi-omics databases that enables AI systems
Create comprehensive schema documentation: table descriptions, column-level annotations, relationship mappings, business logic rules, and domain-specific constraints (e.g., statistical thresholds, unit conventions, experimental design metadata)
Develop gold-standard question/SQL pairs for each major database, in collaboration with computational biologists and Generative AI Engineers, to serve as training data, few-shot examples, and evaluation benchmarks
Build and maintain a data dictionary and ontology mapping layer that translates how scientists think and speak about data (gene names, pathway terms, assay types) into how the data is physically stored
AI-Ready Data Products Build and manage vector embedding pipelines for scientific documents, study metadata, and structured data descriptions to power RAG-based retrieval
Build integration pipelines that connect heterogeneous data sources — omics databases, internal publications, electronic lab notebooks, assay results, and clinical annotations — into a unified, queryable layer
Develop and enforce metadata standards that ensure new data sources are AI-accessible from the point of ingestion, not retroactively
Design data products that serve multiple consumption patterns: direct SQL access for computational biologists, structured feeds for ML training pipelines, and semantic interfaces for LLM-powered tools

Benefits

Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance).
In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).

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