Enterprise Quality Data Analyst

About the position

Johnson & Johnson is currently recruiting for an Enterprise Quality Data Analyst! This position will be located in Raritan, NJ. 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.

Responsibilities

• Serve as part of the EQ organization's QOPS Pillar, curating managed datasets, dashboards, and insights to help the QOPS organization achieve its strategic objectives.
• Collaborate with partners and stakeholders to build metrics and data solutions that align with QOPS management and other key pillars within the organization.
• Contribute to the architectural processes and technology solutions that provide data and visualizations, driving outcomes that support the QOPS strategy.
• Design, develop, and implement capabilities for the continuous improvement of J&J's QOPS with an emphasis on Computer System Validation (CSV) and data integrity.
• Proactively identify quality issues through robust data analysis and analytical insights.
• Conduct continuous monitoring of process and system health to ensure ongoing compliance and optimization.
• Utilize internal processes and tools to execute daily tasks efficiently, while continuously seeking opportunities to enhance and reuse existing data solutions across the enterprise.
• Conduct data analysis experiments, track successes through to execution, and document lessons learned from failures.
• Monitor QOPS platforms that support the team's business intelligence tools to guarantee reliable solutions.
• Foster strong relationships with other teams (e.g., Data Science) to leverage organizational assets effectively.
• Act as a consultant to other teams within QOPS, challenge the status quo, and provide technical leadership in the data analytics domain.
• Stay current with technological trends (e.g., AI, BI) to drive best practices within QOPS.