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 partnership with Product Reliability Engineering (PRE) and support teams.

· Ensure service demand and ticket volumes remain aligned with consumption-based operating model assumptions and budget expectations.

· Drive continuous improvement initiatives across Service Maintenance & Operations teams.

· Engage with Service Maintenance & Operations managers and leads to share best practices and standardize processes.

· Support operational maturity initiatives across monitoring, observability, support effectiveness, and reliability practices.

Service Reliability & Quality Engineering

· Drive service quality measurement, service health visibility, and continuous reliability improvements across the R&D portfolio.

· Partner with Product Teams, Operations Teams, and PRE teams to improve service reliability and support outcomes.

· Support observability, event management, and service health monitoring initiatives.

· Identify recurring issues and reliability risks through data analysis and trend review.

· Support Problem Management processes through root cause analysis and operational insights.

· Contribute to application lifecycle quality and operational readiness activities.

· Establish and monitor service quality standards and performance indicators.

· Support continuous improvement efforts that increase stability, reliability, and customer satisfaction.

Application Portfolio & Data Governance

· Maintain oversight of the R&D application portfolio under support.

· Ensure CMDB accuracy, completeness, and governance for supported applications and services.

· Validate application ownership, metadata, relationships, and service mappings.

· Partner with service owners and support teams to improve configuration data quality.

· Leverage CMDB and operational data to improve reporting, service insights, and support effectiveness.

AI & Digital Enablement

· Serve as a key enabler of AI-driven support experiences and self-service transformation.

· Ensure knowledge assets support R&D AI, Copilot, and Agent strategies.

· Partner with digital transformation teams to identify opportunities for AI-enabled operational improvements.

· Support deployment of operational intelligence capabilities that improve decision quality and service outcomes.

· Promote trusted data and knowledge foundations required for scalable AI adoption.

Success Measures

Outcome area

Measures of success

Knowledge Management

• •Knowledge quality and completeness
• •Knowledge article utilization
• •Knowledge governance compliance
• •Self-service adoption rates
• •Support readiness metrics

Operational Intelligence

• •Leadership adoption of dashboards and insights
• •Data quality and reporting effectiveness
• •Identification and execution of improvement opportunities
• •Increased visibility into operational performance

Continuous Improvement

• •Reduction in recurring issues
• •Problem Management effectiveness
• •Automation opportunities identified and