Experienced Engineer, Data & Quality

Johnson & Johnson - Experienced Engineer, Data & Quality

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 implemented

• Operational efficiency improvements

• Service Quality & Reliability

• Service health visibility

• Reliability and stability improvements

• Faster issue identification and resolution

• Improved customer experience metrics

• AI Readiness

• AI-ready capabilities and readiness metrics

• Application Portfolio & Data Governance

• CMDB data quality and governance scores

• Data accuracy and usage metrics

• Note: This posting includes details about responsibilities, regulatory considerations, and cross-functional collaboration essential to the role. Anticipated close date provided for application window: Oct 15 2026.