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.

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Job Description Summary 

Johnson & Johnson is recruiting for a Lead, Data Engineering to support the MedTech Supply Chain data ecosystem. Reporting to the Senior Manager, Ecosystem Product Owner, this role will lead the physical engineering and implementation of secure, scalable, governed, and reusable data capabilities that enable trusted data products and downstream business consumption. 

The Lead will partner closely with the Ecosystem Product Owner, data product teams, architecture, platform engineering, and governance stakeholders. The role is accountable for translating approved functional direction and architecture into production-ready pipelines, data models, integrations, and platform services, while establishing the engineering practices required for reliable operation, performance, and lifecycle management. 

Key Responsibilities 

• Lead the detailed design, build, testing, deployment, and ongoing support of physical data pipelines, transformations, integrations, and data services across the MedTech Supply Chain data ecosystem. 

• Translate functional requirements, logical data mappings, architecture standards, and product priorities into engineering designs, technical specifications, delivery estimates, and executable backlog items. 

• Design and implement scalable ingestion, transformation, orchestration, storage, and serving patterns for batch, streaming, API, and event-driven data use cases, as applicable. 

• Develop and maintain physical data models, schemas, transformation logic, interface contracts, and reusable engineering components that support governed data products and consistent downstream consumption. 

• Establish and enforce engineering standards for source control, code review, automated testing, CI/CD, environment promotion, infrastructure configuration, observability, and technical documentation. 

• Implement data quality controls, reconciliation checks, lineage capture, metadata integration, auditability, and operational monitoring in alignment with governance and product requirements. 

• Optimize data workloads for reliability, scalability, performance, and cost, and proactively address technical debt, processing bottlenecks, and platform constraints. 

• Partner with architecture, platform, privacy, and compliance teams to ensure technical solutions meet enterprise standards and applicable control requirements. 

• Own engineering readiness for releases, including test evidence, deployment plans, production validation, incident response procedures, runbooks, and operational handoff. 

• Lead root-cause analysis and resolution for data pipeline, integration, platform, and production reliability issues, and implement preventive improvements. 

• Coordinate dependencies across data engineers, platform teams, source-system owners, and consuming applications to support predictable delivery and clear technical ownership. 

• Provide technical direction, coaching, and quality oversight to assigned engineers and delivery partners, promoting reusable patterns, disciplined execution, and continuous improvement. 

Qualifications 

• A minimum of a Bachelor’s degree in computer science, engineering, information systems, data management, or a related field is required; an advanced degree is preferred. 

• A minimum of 5 years of progressive experience in data engineering, software engineering, data platform delivery, systems integration, cloud data solutions, or a related technical discipline is required. 

• Hands-on experience designing, building, testing, deploying, and supporting production-grade data pipelines, transformations, integrations, and physical data models is required. 

• Experience with modern data platforms, distributed processing, cloud storage, orchestration, SQL, APIs, source control, automated testing, and CI/CD practices is required. 

• Experience implementing operational monitoring, data quality controls, metadata, lineage, security, access controls, and auditability within data solutions is required. 

• Experience partnering with product owners, architects, platform teams, cybersecurity, privacy, governance, and business stakeholders in a matrixed enterprise environment is required. 

• Experience operating in a regulated healthcare, life sciences, medical technology, GxP, CSV, or similarly controlled environment is preferred. 

• Demonstrated ability to lead technical delivery, manage ambiguity, resolve complex engineering issues, and communicate technical tradeoffs in clear business terms is required. 

• Technical leadership, coaching, or formal/informal team direction experience is preferred. 

Preferred Skills 

• Cloud and lakehouse data engineering 

• Data pipeline orchestration and systems integration 

• Physical data modeling and transformation design 

• SQL, Python, APIs, and reusable engineering frameworks 

• DevOps, CI/CD, automated testing, and infrastructure configuration 

• Data quality, metadata, lineage, observability, and production operations 

• Agile delivery, estimation, backlog refinement, and sprint execution 

• Secure engineering, privacy, compliance, and enterprise technology governance 

• Technical leadership, problem solving, and cross-functional collaboration 

Other 

• This position may require up to 10% domestic or international travel based on business needs. 

• The anticipated job function, job sub-function, level, and final position title should be confirmed with Human Resources and the hiring organization prior to posting. 

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