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Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Engineering

Job Category:

People Leader

All Job Posting Locations:

Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

We are searching for the best talent for a Director, R&D Data Systems to be located in Titusville, NJ, Spring House, PA or Raritan, NJ.

The Director, Cross R&D Data Systems, Innovative Medicines is responsible for leading shared data technology capabilities that enable trusted, governed, discoverable, interoperable, and reusable data across the Innovative Medicine R&D ecosystem. The role ensures that data platforms, quality controls, cataloging, master data, ingestion, transformation, and cross-functional data products are operated as enterprise-grade capabilities that support analytics, AI, GenAI, regulatory, safety, discovery, development, and operational use cases.

This role partners across data product teams, analytics/model teams, functional data owners, and business stakeholders to run an integrated Data & AI operating model. The role translates data strategy, governance requirements, data product needs, and business priorities into scalable platforms, data services, standards, scorecards, and operating practices.

The role is accountable for data quality and scorecards, data governance and standards, data catalog, master data management, R&D data platforms, data ingestion and transformation services, data virtualization platforms, and cross-functional data products across Innovative Medicine R&D.

Key Responsibilities

Data Quality and Scorecards

• Define and operate data quality frameworks, scorecards, dashboards, thresholds, remediation routines, and executive reporting across priority R&D data domains and products.
• Partner with DDSAI (R&D Data Science Team), data owners, product teams, and business functions to define fit-for-purpose data quality rules, ownership, permitted use, and quality acceptance criteria.
• Establish automated quality monitoring for completeness, accuracy, timeliness, uniqueness, consistency, lineage, and domain-specific quality expectations.
• Translate data quality scorecard insights into remediation plans, product backlog priorities, governance decisions, and measurable improvements.
• Create transparency into data readiness for analytics, AI/GenAI, operational reporting, regulatory, safety, and scientific use cases.

Data Governance and Standards

• Implement data governance standards, decision rights, access workflows, data contracts, metadata expectations, permitted-use controls, lifecycle practices, and policy adherence across Cross R&D data systems.
• Partner with DDSAI data governance leaders, privacy, legal, Cybersecurity, quality, architecture, and business data owners to ensure governance is embedded into platforms and delivery workflows.
• Enforce standards for data domains, naming conventions, lineage, quality thresholds, stewardship, data sharing, retention, and compliant use.
• Establish governance routines that connect intake, prioritization, roadmap planning, data product ownership, standards compliance, and value realization.
• Enable consistent governance for structured, unstructured, semantic, operational, scientific, clinical, regulatory, and external data assets.

Data Catalog, Metadata and Lineage

• Lead data catalog capabilities that improve discoverability, business context, technical metadata, ownership, lineage, permitted use, and reuse of R&D data assets.
• Integrate cataloging into data product delivery, ingestion workflows, transformation services, governance checkpoints, and operational support processes.
• Partner with DDSAI and data product owners to capture business purpose, data contracts, quality thresholds, semantic definitions, permitted use, and consumption patterns.
• Ensure catalog metadata connects source systems, transformations, data products, APIs, reports, AI/GenAI use cases, and downstream consumption.
• Drive adoption of catalog and lineage practices through enablement, automation, standard workflows, and transparent metrics.

Master Data Management

• Lead technology capabilities supporting master data management across critical R&D entities, including canonical entities, reference data, identifiers, hierarchies, matching, stewardship workflows, and curation services.
• Partner with DDSAI, data governance, business data owners, and enterprise data teams to align MDM strategy, domain ownership, data curation, platform capabilities, and integration patterns.
• Enable high-quality master data for cross-functional interoperability, analytics, AI, reporting, workflow automation, and business process consistency.
• Establish operational practices for MDM platform health, data quality, stewardship queues, lifecycle controls, integration reliability, and issue remediation.
• Promote reuse of enterprise MDM services and canonical entities across R&D platforms and data products.

R&D Data Platforms

• Own strategy, operations, modernization, and adoption of R&D data platforms like Snowflake, Databricks, GCP Big query that support data products, analytics, AI/GenAI, reporting, scientific computing, and cross-functional business needs.
• Partner with Technology Services, architecture, security, DDSAI, product teams to provide scalable, secure, performant, and cost-effective platform services.
• Ensure data platforms meet service expectations for availability, resiliency, observability, access management, compliance, privacy, and lifecycle management.
• Drive simplification, standardization, platform reuse, cloud optimization, automation, and technical debt reduction across the R&D data ecosystem.
• Maintain clear platform roadmaps, service levels, investment priorities, funding needs, and value realization metrics.

Data Ingestion and Transformation Platforms and Services

• Lead ingestion and transformation platforms and services for R&D data, including raw data onboarding, technical curation, pipeline engineering, orchestration, monitoring, and troubleshooting.
• Establish reusable patterns for ingestion, transformation, validation, data contracts, observability, exception handling, and release management.
• Partner with data product teams to ensure pipelines support quality thresholds, lineage, metadata capture, governance linkage, and downstream consumption needs.
• Drive operational excellence across data pipelines, including reliability, performance, failure recovery, supportability, cost optimization, and SLA management.
• Enable scalable integration of internal, external, structured, unstructured, scientific, clinical, regulatory, safety, and operational data sources.

Data Virtualization Platforms

• Lead data virtualization capabilities that provide governed, secure, performant, and reusable access to distributed R&D data without unnecessary replication.
• Define patterns for virtualized access, query federation, semantic access, caching, security, performance optimization, and lifecycle management.
• Partner with architecture, security, data engineering, analytics, and product teams to determine when virtualization, replication, APIs, or data products are the right pattern.
• Monitor platform performance, adoption, usage, latency, access patterns, cost, and reliability across virtualized data services.
• Ensure data virtualization supports self-service analytics, AI/GenAI, operational reporting, and cross-functional data product consumption within governance guardrails.

Cross-Functional Data Products

• Lead delivery and lifecycle management of cross-functional R&D data products that serve Discovery, Development, Regulatory Affairs, Medical Safety, Quality, Finance, portfolio planning, and R&D operations.
• Partner with DDSAI and business data product owners on product framing, business value, OKRs, domain knowledge, data contracts, quality thresholds, ownership, and adoption plans.
• Ensure cross-functional data products are built on secure, scalable, governed platforms with clear lineage, quality controls, metadata, support models, and consumption patterns.
• Translate business needs into reusable data services, APIs, semantic assets, dashboards, data products, and AI-ready data capabilities.
• Manage roadmaps, backlog, release planning, lifecycle management, funding, adoption, value realization, and continuous improvement for shared data products.

Integrated DDSAI/JJT Ways of Working

• Operationalize integrated DDSAI (Data Science Team)/JJT (Technology Team) ways of working for Data & AI initiatives, with clear accountability across data product strategy, governance, engineering, platform operations, roadmap planning, E2E testing, financial planning, and lifecycle management.
• Clarify collaboration points across DDSAI, Technology Services, Cybersecurity, Enterprise Architecture, business data owners, product teams, and external partners.
• Use intake, portfolio governance, systems of record, and leadership reporting to create transparency into data initiatives, milestones, risks, dependencies, financials, and value realization.
• Ensure data platform and product delivery balances speed, standardization, governance, security, quality, user experience, and patient/business impact.
• ·Drive a culture of product ownership, shared accountability, data stewardship, reuse, quality, and measurable outcomes across Cross R&D data systems.

Qualifications

• Master’s degree in Computer Science, Information Technology, Data Science, Engineering, or related field; advanced degree preferred.
• 10+ years of progressive technology leadership experience with significant focus on data platforms, data engineering, data governance, data products, MDM, cataloging, analytics platforms, or enterprise data transformation.
• 10+ years in data platform engineering, data architecture, or enterprise data management
• ·Strong background in designing and implementing cloud-based data platforms and capabilities , and enterprise MDM solutions.
• Experience in life sciences, healthcare, biotech, pharmaceuticals, or other highly regulated environments preferred.
• Demonstrated expertise in data platforms, ingestion and transformation services, data quality, metadata management, cataloging, lineage, access controls, data virtualization, and master data management.
• Strong understanding of data product management, data contracts, governance workflows, privacy, security, compliance, AI-ready data, and platform operating models.
• Proven ability to lead cross-functional teams and influence DDSAI, technology, security, architecture, data engineering, product, platform, vendor, and business stakeholders.
• Strong financial and operational discipline, including experience managing platform costs, service health, SLAs, adoption, portfolio priorities, and measurable value realization.
• Exceptional communication skills with the ability to translate complex data platform and governance topics into clear business decisions and outcomes.

Professional Experience

• Proven success building and operating enterprise-grade data platforms, data products, ingestion pipelines, transformation services, cataloging, MDM, and data quality capabilities.
• Experience establishing operating models for governed, reusable, high-quality data assets across business functions and technology teams.
• Demonstrated ability to partner with data science, analytics, business, and technology organizations to enable AI-ready, discovery-ready, development-ready, regulatory-ready, and operationally trusted data.
• Experience managing critical data services with reliability targets, operational metrics, issue remediation, cost optimization, and service improvement routines.
• Strong track record driving adoption of shared platforms, common standards, self-service patterns, metadata practices, governance workflows, and data product lifecycle management.
• Skilled in managing vendor ecosystems, enterprise data platforms, technology dependencies, data risk tradeoffs, and multi-year modernization roadmaps.
• Strong executive communication and influencing skills, with the ability to translate data landscape complexity into clear decisions, investment priorities, and business value.

#JNJTech

#LI-Hybrid

 

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act. 

Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, please contact us via https://www.jnj.com/contact-us/careers or contact AskGS to be directed to your accommodation resource.

Required Skills:

 

 

Preferred Skills:

Advanced Analytics, Agility Jumps, Data Engineering, Data Governance, Data Modeling, Data Privacy Standards, Data Quality, Data Science, Developing Others, Execution Focus, Hybrid Clouds, Inclusive Leadership, Leadership, Program Management, Stakeholder Engagement, Succession Planning, Technical Development, Technologically Savvy

 

 

The anticipated base pay range for this position is :

$150,000 - $258,740

Additional Description for Pay Transparency:

Subject to the terms of their respective plans, employees and/or eligible dependents are eligible to participate in the following Company sponsored employee benefit programs: medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, and group legal insurance. Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)). This position is eligible to participate in the Company’s long-term incentive program. Subject to the terms of their respective policies and date of hire, Employees are eligible for the following time off benefits: Vacation –120 hours per calendar year Sick time - 40 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year Holiday pay, including Floating Holidays –13 days per calendar year Work, Personal and Family Time - up to 40 hours per calendar year Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child Condolence Leave – 30 days for an immediate family member: 5 days for an extended family member Caregiver Leave – 10 days Volunteer Leave – 4 days Military Spouse Time-Off – 80 hours  Additional information can be found through the link below.  https://www.careers.jnj.com/employee-benefits