Director R&D Data Systems

Johnson & Johnson Innovative Medicine•Hopewell Township, NJ

2d•$150,000 - $258,740•Hybrid

About The Position

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.

Requirements

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.
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.

Nice To Haves

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

Responsibilities

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.
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.
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.
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.
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.
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.
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.
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.
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.

Benefits

medical
dental
vision
life insurance
short- and long-term disability
business accident insurance
group legal insurance
consolidated retirement plan (pension)
savings plan (401(k))
long-term incentive program
Vacation –120 hours per calendar year
Sick time - 40 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