Director R&D Data Systems

About this role

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world.  We provide an inclusive work environment where each person is considered as an individual.  At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

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, Data

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