Lead, Ecosystem Engineer

Johnson & Johnson Co.; Bogotá; On-site; COP 90,000,000 - 150,000,000; Full time

Job summary

Johnson & Johnson is recruiting 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 data capabilities for trusted data products.

The Lead will partner with data product teams, architecture, platform engineering, and governance stakeholders to translate direction into production-ready pipelines, models, integrations, and

Qualifications

• Bachelor's degree in computer science, engineering, information systems, data management, or related field.
• 5+ years of progressive experience in data engineering or related technical discipline.
• Hands-on experience designing, building, testing, deploying production-grade data pipelines.
• Experience with modern data platforms, cloud storage, orchestration, SQL, APIs, and CI/CD.
• Experience with data quality controls, lineage, governance, and security within data solutions.
• Experience collaborating with product owners, architects, and governance teams in a matrixed environment.
• Experience in regulated healthcare/medical technology environments is preferred.
• Ability to lead technical delivery, manage ambiguity, and communicate tradeoffs clearly.

Responsibilities

• Lead the design, build, testing, deployment, and support of data pipelines and data services across the MedTech Supply Chain data ecosystem.
• Translate requirements into engineering designs, specifications, and executable backlog items.
• Design scalable ingestion, transformation, orchestration, storage, and serving for batch, streaming, API, and event-driven use cases.
• Develop and maintain physical data models, schemas, and reusable engineering components.
• Establish and enforce engineering standards for source control, automated testing, CI/CD, observability, and documentation.
• Implement data quality, lineage, metadata integration, auditability, and operational monitoring.
• Optimize workloads for reliability, performance, and cost; address technical debt and bottlenecks.
• Collaborate with architecture, platform, privacy, and governance teams to meet enterprise standards.
• Own engineering readiness for releases, including evidence, deployment plans, and runbooks.
• Lead root-cause analysis and preventive improvements for data pipeline issues.
• Coordinate dependencies across data engineers, platform teams, and stakeholders.

Skills

Data engineering

Cloud data platforms

SQL

Python

APIs

CI/CD

Data governance

Agile delivery

Team leadership

Education

Bachelor's degree in Computer Science

Tools

Git

CI/CD tools

APIs testing tools

Job description

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:

Marketing

Job Sub Function:

Product Management

Job Category:

Professional

All Job Posting Locations:

Bogotá, Distrito Capital, Colombia, São Paulo, Brazil

Job Description:

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.

Preferred Skills:

Advanced Analytics, Agility Jumps, Compliance Management, Data Architecture Development, Data Engineering, Data Governance, Data Modeling, Data Privacy Standards, Data Quality, Data Science, Developing Others, Hybrid Clouds, Inclusive Leadership, Leadership, Motivating People, Standard Operating Procedure (SOP), Technologically Savvy, Workflow Management