Manager Data Engineering

Johnson & Johnson MedTech is dedicated to innovating healthcare solutions, particularly in Orthopaedics. The Data Engineering Manager will lead the strategy and execution of data engineering initiatives to modernize the data landscape and support digital products within the Orthopaedics Supply Chain.

Responsibilities:

• Lead the Data Foundation initiative to modernize the enterprise data ecosystem through a scalable lakehouse architecture and cloud-based data platform capabilities
• Define the data engineering strategy, target architecture, and reusable pipeline frameworks needed to deliver governed, high-quality data products
• Develop the strategy for a Data Supermarket that delivers business-ready data products for use across multiple functions
• Translate complex business requirements and technical challenges into scalable architecture decisions and executable delivery plans
• Provide technical leadership for business separation activities, ensuring alignment to future-state operating models and platform continuity
• Establish and enforce best practices for:
• Data modeling (dimensional, Data Vault 2.0)
• Pipeline design, modularity, and reuse
• Engineering standards and quality controls
• Establish and scale data governance, data quality, and observability practices, including monitoring, lineage, reliability, and service-level expectations
• Define and implement automated testing strategies for data pipelines, including validation, data quality controls, and CI/CD integration
• Lead development and orchestration using Databricks, Python, SQL, dbt, Airflow, and cloud-native tools
• Partner with vendors and internal teams to manage delivery, enforce standards, and drive outcome-based execution
• Collaborate across Product, Supply Chain business, AI/ML, Data Governance, and IT teams to deliver measurable business impact

Requirements:

• A Bachelor's or Master's degree in Computer Science, Engineering, Applied Mathematics, or in a related field
• Minimum of 8 years of experience in data engineering, including 2 or more years in leadership or people management roles
• Strong hands-on technical experience with: Databricks, PySpark, Spark SQL
• Python and SQL development
• Cloud platforms (Azure preferred, AWS acceptable)
• Proven expertise in: Modern data architecture and platform design
• Dimensional modeling and Data Vault 2.0
• Experience with: dbt, Airflow, Azure Data Factory (or equivalent tools)
• CI/CD pipelines, automation frameworks, and testing practices
• Required experience designing and implementing automated testing strategies for data engineering pipelines, including: End-to-end pipeline validation
• Data quality and integrity testing
• Integration with CI/CD pipelines
• Experience leading distributed teams and partnering effectively with external vendors and cross-functional stakeholders
• Experience supporting Agile product delivery teams and mentoring engineers in complex, fast-paced technical environments
• Familiarity with end-to-end supply chain domains such as planning, manufacturing, procurement, and distribution within MedTech or a similar regulated industry
• Knowledge of enterprise systems of record such as ERP, MES, and PLM, including experience working with fragmented or legacy data environments
• Experience with: Lakehouse architecture and Delta Lake
• Data observability and monitoring frameworks
• Experience collaborating with AI/ML and advanced analytics teams to enable scalable data and model-ready pipelines
• Knowledge of Power BI or enterprise BI platform
• Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Cursor, or similar) to: Accelerate pipeline development
• Automate engineering workflows
• Exposure to code generation, pipeline automation, or low-code data engineering accelerators