Principal Data Engineer, Biologics Discovery AI-Ready Data

Johnson Johnson; United States; On-site; USD 117,000 - 201,000; Full time

Benefits offered by this job

Medical benefits

401(k) retirement plan

Paid time off

Job summary

Johnson & Johnson Innovative Medicine is seeking a Principal Data Engineer to shape discovery data products and models across Biologics Discovery. You will lead data integration, harmonization, and analytics readiness, partnering with scientists, AI/ML teams, and technology partners.

Based in Spring House, PA or NJ offices, you will drive data standards, provenance, and scalable architectures, translating complex scientific needs into durable data assets and AI-enabled workflows.

Qualifications

• Degree in Computer Science, Data Science, Engineering, or related computational field.
• 8+ years (Bachelor's), 5+ years (Master's), or 3+ years (Ph.D.) of experience designing data products.
• Strong Python and SQL, with cloud data platforms experience for analytics and ML.
• Experience applying FAIR data principles, metadata, and data lineage.

Responsibilities

• Design and deliver AI-ready discovery data products for Biologics Discovery.
• Define scalable integration requirements, transformations, and data schemas.
• Translate scientific needs into data product specifications and contracts.
• Lead data standards adoption, governance, and metadata practices.

Skills

Data Engineering

Data Modeling

Data Governance

Python

SQL

Cloud Data Platforms

Communication

Education

Degree in Computer Science, Data Science, Engineering, or related computational field

Tools

Snowflake

AWS

Azure

BigQuery

Job description

Johnson & Johnson Innovative Medicine is seeking a Principal Data Engineer to shape discovery data products and models across Biologics Discovery. You will lead data integration, harmonization, and analytics readiness, partnering with scientists, AI/ML teams, and technology partners.

Based in Spring House, PA or NJ offices, you will drive data standards, provenance, and scalable architectures, translating complex scientific needs into durable data assets and AI-enabled workflows.