Experienced Engineer, Data Management

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.

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Job Description:

The Experienced Engineer designs and delivers trusted, reusable data capabilities by integrating enterprise data products, engineering scalable batch and real-time pipelines, improving data quality, and enabling analytics and AI use cases. The engineer partners with technology teams to translate moderately complex requirements into reliable, governed, and supportable data solutions.

Key Responsibilities

• Architect, build, test, deploy, and support batch and real-time data pipelines and aggregation processes that deliver accurate, timely data for analytics and reporting.
• Prepare moderately complex structured and unstructured data by ingesting, parsing, profiling, wrangling, cleansing, standardizing, matching, and organizing it for downstream use.
• Implement data quality rules and controls; investigate defects, identify root causes, coordinate remediation, and monitor quality trends against agreed standards.
• Develop custom programs, reusable data assets, and platform components that address defined business intelligence, analytics, and data science needs.
• Integrate data across enterprise platforms, document source-to-consumption lineage and metadata, and support governed master and reference data.
• Automate repeatable workflows and operational controls to improve scalability, reliability, efficiency, and user experience.
• Apply responsible AI, information security, data privacy, Johnson & Johnson’s Credo, and Leadership Imperatives in day-to-day delivery and decision-making.

Qualifications

· Bachelor’s degree or equivalent in Computer Science, Engineering, Information Systems, Statistics, Mathematics, Data Science, Economics, Finance, or a related quantitative field.

· Minimum of 5 years of related experience in data engineering, data management, analytics, business intelligence, or a comparable technical field.

· Hands-on experience architecting automated data flows and building production data pipelines using Python, R, or Scala and SQL, or ETL tools such as Alteryx, KNIME, or similar platforms.

· Experience integrating enterprise data sources and evaluating data quality through profiling, validation, cleansing, standardization, monitoring, and remediation.

· Strong analytical skills and hands-on experience working with structured and unstructured data, quantitative analysis, and data visualization.

· Experience with enterprise data platforms and cloud technologies, including Databricks and Azure, AWS, GCP, or comparable environments.

· Working knowledge of data modeling, metadata, lineage, semantic layers, master and reference data, and governance controls.

· Experience using analytical or visualization tools such as Tableau, Power Apps, Power BI, Qlik, Cognos, Shiny, Dash, or Streamlit.

· Working knowledge of automation, AI-assisted analytics, machine learning, or GenAI concepts, with the ability to apply responsible AI and sensitive-data handling requirements.

· Strong collaboration, communication, problem-solving, and stakeholder management skills, including the ability to translate technical concepts into business value.

· Fluency in English, written and spoken.

Preferred Skills

· Experience with procurement data domains and source-to-settle processes, including supplier, spend, contract, category, sourcing, and purchasing data.

· Knowledge of procurement and ERP platforms such as SAP, Ariba, VISION, eICD, Cognos, or comparable systems.

· Experience applying AI or machine learning to data quality, stewardship, entity resolution, anomaly detection, or analytics use cases.

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