Manager, 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: Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: People Leader All Job Posting Locations: Horsham, Pennsylvania, United States of America, Titusville, New Jersey, United States of America Job Description: We are searching for the best talent for Manager, Data Quality About Innovative Medicine Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine Purpose: The Manager, Data Quality is accountable for improving the integrity of data across the One Data Platform (ODP). The role leads data quality operations, business-rule validation, data profiling, monitoring, and issue remediation to ensure enterprise data products are accurate, complete, consistent, timely, and fit for use in reporting, analytics, artificial intelligence, and business decision-making. This role provides functional leadership for delivery partners, establishes priorities, develops team capabilities, and is accountable for the quality and timeliness of data quality services across ODP. The Manager partners with commercial business teams, data product owners, master data management, data governance, analytics teams, and external data providers to establish ownership, embed preventive controls, resolve recurring issues, and improve platform trust and adoption across J&J Innovative Medicine You will be responsible for: Data Quality Strategy & Execution Lead the execution and continuous improvement of the data quality strategy across datasets, prioritizing critical commercial and enterprise data domains based on business impact and risk. Define and maintain data quality standards, controls, acceptance criteria, and monitoring practices. Establish quality requirements and readiness checks for onboarding new data sources and data products. Develop and track data quality metrics, scorecards, service expectations, and improvement plans. Help make data business-ready and AI-ready through consistent, measurable quality controls. Explore responsible use of AI-enabled methods to identify, explain, prioritize, and prevent data quality issues. Data Quality Operations Lead day-to-day data profiling, validation, reconciliation, exception management, and issue triage across ODP. Monitor data quality dashboards and identify trends, recurring defects, and operational risks. Coordinate root-cause analysis with data engineering, source-system teams, data providers, and business owners. Drive corrective and preventive actions, track issue ownership, and ensure timely remediation of priority defects. Validate that fixes address the underlying cause and do not create downstream business impact. Work with internal teams and external data providers to improve the quality, consistency, and timeliness of inbound commercial data. Partner with business teams supporting customer, account, affiliation, sales, prescription, market access, payer, field, targeting, alignment, and incentive compensation data. Qualifications / Requirements: Required Bachelor's degree in information systems, data analytics, computer science, engineering, business, life sciences, or related field. 5+ years of progressive experience in data quality or data management, including hands-on responsibility for defining controls, monitoring data health, and leading issue remediation in an enterprise data environment. Strong knowledge of the pharmaceutical industry and commercial pharma business processes, with practical domain knowledge of how commercial data supports customer engagement, field execution, reporting, analytics, and decision-making. Hands-on knowledge of commercial pharmaceutical data, including several of the following: Professional and account master data, affiliations, sales, claims, market access, payer, field activity, targeting, alignment, and incentive compensation. Experience defining data quality rules and applying core dimensions such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. Experience supporting enterprise data platforms, cloud data environments, analytics platforms, or governed data products. Strong analytical, problem-solving, communication, facilitation, and stakeholder-management skills. Demonstrated experience leading cross-functional initiatives, setting priorities, influencing senior stakeholders, and coaching team members or delivery partners. Experience supporting data used for AI, machine learning, advanced analytics, or semantic data products. Experience applying AI-assisted techniques for data profiling, anomaly detection, rule generation, matching, classification, root-cause analysis, and issue prioritization. Proficiency using approved AI assistants to accelerate analysis, SQL creation, documentation, and issue investigation while protecting confidential pharmaceutical data. Technical Skills SQL or scripting language: Strong hands-on ability to profile large datasets, validate business rules, reconcile data across sources, investigate anomalies, and support root-cause analysis. Data quality controls: Experience designing and implementing automated checks for accuracy, completeness, consistency, timeliness, validity, and uniqueness. Data pipelines and integration: Working knowledge of ETL/ELT processes, source-to-target mapp