Experienced Engineer, Data & Quality

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 Technology Product & Platform Management Job Sub Function Reliability Engineering Job Category Scientific/Technology All Job Posting Locations Raritan, New Jersey, United States of America Job Description We are searching for the best talent for a Experienced Engineer, Data & Quality to be located in Raritan, NJ Position Summary The Senior Analyst, Data & Quality Engineering is responsible for advancing Knowledge Management, Operational Intelligence, Service Reliability, and Continuous Service Improvement across the MedTech R&D application portfolio.This role serves as a critical cross-functional enabler by ensuring trusted knowledge assets, actionable operational insights, high-quality configuration data, and data-driven improvement initiatives that enhance service performance and user experience.The position enables future-state R&D support models through scalable Knowledge Management practices, Operational Intelligence capabilities, AI-ready content foundations, and continuous improvement disciplines. The role supports improved decision-making, accelerated issue resolution, increased self-service adoption, and more efficient support operations across the R&D landscape. Key Responsibilities Knowledge Management Leadership & Governance Own and mature R&D Knowledge Management practices, governance, and standards. Receive knowledge deliverables from Service Transition activities and ensure operational readiness for support teams. Manage the knowledge portfolio across supported R&D applications and services. Ensure knowledge assets are maintained, validated, and effectively utilised across Incident Management and Service Request Management processes. Be accountable for Knowledge Management KPIs, adoption metrics, content health, process compliance, and governance adherence. Establish knowledge lifecycle management processes and content quality standards. Sponsor and drive continuous Knowledge Management improvement initiatives. Identify and remediate knowledge gaps impacting support effectiveness and user self-service. Promote Knowledge Management best practices across support organizations and vendor teams. Coach vendor resources using a coach-the-coach model to improve knowledge quality and usage. Conduct knowledge article quality reviews and remediation activities as required. Represent Application Maintenance and R&D Support within Knowledge Management councils and governance forums. Partner with AI, Copilot, and Agent initiatives to ensure knowledge repositories are trusted, structured, and optimized for AI consumption. Operational Intelligence & Analytics Develop and maintain operational intelligence capabilities across the R&D application portfolio. Create leadership dashboards, reporting frameworks, scorecards, and performance insights. Analyze incident, request, operational, application, and support data to identify trends and opportunities. Translate operational data into actionable recommendations and business decisions. Support leadership decision-making through data-driven insights and reporting. Provide portfolio-level visibility into service quality, operational performance, and improvement opportunities. Identify opportunities for operational optimization, automation, and workload reduction. Support forecasting, capacity planning, and demand analysis activities. Continuous Service Improvement & Operational Excellence Monitor and measure the quality, effectiveness, and efficiency of R&D support operations using defined KPIs and service management metrics. Benchmark operational performance and identify opportunities for improvement. Analyze incident, request, and support trends to identify chronic issues and systemic risks. Identify ticket patterns that should trigger Problem Management investigations and chronic problem processes. Evaluate ticket reassignment trends, service bottlenecks, and support inefficiencies. Develop recommendations that improve service quality, customer experience, and operational efficiency. Define business requirements for automation opportunities and process improvements in partne #J-18808-Ljbffr Johnson Johnson