About this opportunity
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.
Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s):
Brazil R-108156, Colombia R-106118
Remember, whether you apply to one or all of these requisition numbers, your applications will be considered as a single submission.
Job Function:
Marketing
Job Sub Function:
Product Management
Job Category:
Professional
All Job Posting Locations:
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.
Required Skills:
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
Job details
How this role compares
Computed from every other active Marketing role in our database, not just this employer's listings.
We currently track 1277 comparable Marketing roles across 157 biopharma companies.
Salary context
295 of 1277 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
Top locations among the 1277 comparable roles
+ 56 more countries
Seniority mix
1002 of 1277 peers have a known seniority level
Therapeutic area mix
225 of 1277 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.