> Markdown version of [/jobs/ext/3551773-experienced-engineer-data-quality](https://www.wearedevelopers.com/jobs/ext/3551773-experienced-engineer-data-quality). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Experienced Engineer, Data & Quality - **Company:** Johnson & Johnson - **Location:** Raritan, NJ, United States - **Experience:** Experienced - **Salary:** $79,000.0 - $127,075.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Application Lifecycle Management, Application Portfolio Management, Configuration Management Databases, Data Governance, Human-Computer Interaction, Knowledge Management, Operational Data Store, Software Maintenance, Microsoft Copilot, Software Engineering, Enterprise Software Applications, Reliability of Systems, Information Technology, Data Analytics, Tools for Reporting, Meditech, Servicenow - **Published:** October 2, 2026 - **Apply:** https://dejobs.org/x/x/AAB582D379814C4C903D14E96FA92B89/job/ ## About the Role * 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 knowledge coverage * Agent effectiveness and adoption * Improved self-service outcomes * Reduced dependency on manual support channels Preferred Qualifications · Bachelor's degree in Information Technology, Engineering, Data Analytics, Business, or a related discipline. · Experience in Knowledge Management, Service Management, Operational Intelligence, Operational Excellence, or Application Support. · Strong analytical and data interpretation skills. · Experience developing dashboards, reporting frameworks, and operational insights. · Experience with ServiceNow, CMDB governance, Knowledge Management, and ITSM processes. · Knowledge of Problem Management, Continuous Improvement, and Service Reliability practices. · Experience working across complex enterprise application portfolios. · Ability to influence and lead initiatives across multiple teams without direct authority. · Strong communication, stakeholder management, and organizational skills. #JNJTECH, Analytical Reasoning, Data Savvy, Green Manufacturing, Human-Computer Interaction (HCI), Mentorship, Process Optimization, Product Knowledge, Product Reliability, Quality Processes, Root Cause Analysis (RCA), Self-Awareness, Six Sigma, Software Development Management, Software Reliability Engineering, Technologically Savvy, Time Management ## Description 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.