Vector Data Engineer

Johnson and Johnson Innovative Medicine (J&J IM), a pharmaceutical company of Johnson & Johnson is recruiting for a Vector Data Engineer.  This position has a primary location of Barcelona, Spain. The secondary location is Madrid. This is a hybrid role. 

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work in teams that save lives by developing the medicines of tomorrow.  

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine   

Position Summary:  

The Vector Data Engineer designs and implements the embedding and semantic-search infrastructure that connects discovery, translational, and clinical data into AI-ready knowledge representations. 

This role bridges multi-omics data engineering and machine-learning infrastructure, enabling scientists and agentic tools to discover biological insights through vector-based search and reasoning. 

Key Responsibilities:  

• Develop scalable pipelines that convert multi-omics and clinical data (e.g., proteomics, transcriptomics, spatial omics, biomarkers) into vectorized embeddings for AI and semantic retrieval. 

• Build and maintain vector databases and hybrid data stores using technologies such as TileDB, Weaviate, or Snowflake Cortex. 

• Collaborate with the Data Transformation Engineers to design standardized data formats suitable for embedding generation and cross-modality mapping. 

• Integrate metadata, ontology terms, and provenance into vector representations to ensure traceability and governance compliance. 

• Partner with AI/ML Team to deploy embeddings supporting agentic reasoning, semantic similarity, and cross-dataset query. 

• Optimize indexing, retrieval, and inference performance across large-scale multi-omics data collections. 

• Evaluate and incorporate emerging representation-learning and knowledge-graph techniques to improve data discoverability and model interoperability. 

Qualifications: 

• MS/PhD in Computer Science, Computational Biology, Data Science, or related field. 

• 3+ years of experience building or maintaining vector or semantic-retrieval infrastructure. 

• Hands-on experience with multi-omics or biomedical data integration (e.g., RNA-seq, proteomics, clinical endpoints). 

• Proficiency in Python and frameworks such as LangChain, Transformers, or sentence-embedding models. 

• Familiarity with TileDB, Snowflake, Weaviate, FAISS, or other vector/array database systems. 

• Understanding of metadata modeling, ontologies (e.g., OBO, UMLS), and FAIR data practices. 

• Strong ability to collaborate across solution architecture, data science, and AI/ML teams. 

Strategic Impact:  

• Multi-omics and clinical data assets transformed into interoperable, vectorized embeddings supporting scientific AI applications. 

• AI can perform semantic queries and reasoning over governed datasets. 

• Vector database infrastructure scales efficiently and complies with governance and lineage standards. 

• Accelerated insight generation across discovery, translational, and clinical domains. 

#JRDDS