Principal Data Engineer, Biologics Discovery

Johnson & Johnson

. Shape how discovery data is structured, connected, and made AI-ready across Biologics Discovery .

Posted 9/18/2026full-timeSpring House • New Jersey • United StatesLead💰 $117,000 - $201,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing AI-ready data products and implementing scalable data architectures, with a strong focus on FAIR data principles and cross-functional collaboration in scientific environments. Proven ability to lead technical initiatives and establish data standards while ensuring data quality and accessibility for advanced analytics.

Highest-signal resume keywords

Python ProficiencySQL ProficiencyData Product DesignFAIR Data PrinciplesTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingAnalytics-Ready DatasetsData IntegrationData Schema DesignData LineageMetadata ManagementCloud Data PlatformsMachine Learning SupportAutomated TestingCI/CD

Soft Skills

Strong Communication SkillsCross-Functional CollaborationTechnical Mentorship

Tools & Technologies

SnowflakeAWSAzureBigQueryContainersOrchestrationMonitoring

Industry Keywords

Biologics DiscoveryPharmaceuticalBiotechnologyLife SciencesHigh-Throughput ExperimentationCROCDMOOntologySemantic TechnologiesKnowledge Graphs

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudPythonSQL

About the role

Key responsibilities & impact

Shape how discovery data is structured, connected, and made AI-ready across Biologics Discovery
Design and deliver AI-ready discovery data products supporting ML, AI, and insight generation
Define scalable integration requirements, transformation patterns, and data schemas for discovery data acquisition, harmonization, and downstream analytics
Translate scientific and analytical requirements into data product specifications, data contracts, acceptance criteria, and delivery requirements
Define access and data consumption patterns for analytics, modeling, and agentic AI workflows
Catalog discovery instruments, data types, and data sources to inform prioritization and integration approaches
Establish and drive adoption of standards and best practices for data quality, provenance, lineage, reproducibility, metadata, and documentation
Partner with ontology, data architecture, platform, and AI teams to make discovery data products connected, discoverable, and suitable for advanced analytics and AI applications
Apply FAIR data principles to make data products reusable, scalable, and interoperable
Serve as a thought leader in scientific data architecture, harmonization, and AI-ready data practices
Collaborate with scientists, AI/ML teams, enterprise Data Strategy & Products, Technology teams, and other stakeholders

Requirements

What you’ll need

Degree in Computer Science, Data Science, Engineering, or a related computational field
8+ years of experience with a Bachelor's degree, 5+ years with a Master's degree, or 3+ years with a Ph.D.
Experience designing and delivering data products, data models, and analytics-ready datasets within pharmaceutical, biotechnology, or life sciences organizations
Deep proficiency in Python and SQL
Hands-on experience designing and implementing reusable and scalable data assets on cloud data platforms such as Snowflake, AWS, Azure, or BigQuery
Experience supporting analytics, machine learning, and AI-driven workflows
Experience applying FAIR data principles, metadata management, controlled vocabularies, data lineage, and provenance practices in scientific data environments
Technical leadership through architecture reviews, mentorship, code reviews, or leadership of complex technical initiatives
Ability to lead complex cross-functional technical initiatives and establish data standards across multiple stakeholder groups
Strong communication skills and seniority to represent the team in architecture, data, and strategy forums
Prior technical mentorship or leadership responsibilities preferred
Experience with ontologies, semantic technologies, knowledge graphs, or ontology-driven data architectures preferred
Experience in biologics discovery, high-throughput experimentation, or external partner (CRO/CDMO) data integration preferred
Experience with automated testing, CI/CD, containers, orchestration, and monitoring preferred

Benefits

Comp & perks

Annual performance bonus
Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Pension plan
401(k) savings plan
Vacation – up to 120 hours per calendar year
Sick time – up to 40 hours per calendar year
Holiday pay, including Floating Holidays – up to 13 days per calendar year
Work, Personal and Family Time – up to 40 hours per calendar year
Inclusive interview accommodations