Principal Data Engineer, Biologics Discovery
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. No Job ID etc. Remove boilerplate. Must preserve all substantive info.
We need to parse the posting and extract relevant sections.
Let's identify content:
- About the role: The posting includes "About The Opportunity" and description. Could be "About the role". We'll put that as About the role
with paragraphs.
- Responsibilities: There's a "Key Responsibilities" section with bullet points. We'll output Responsibilities
with - items.
- Requirements/Qualifications: There's a "Qualifications" section with required and preferred. We can combine into Requirements or Qualifications. The instruction says use headings like Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which to include. Likely include Qualifications (required) and maybe Skills (preferred). But we must not create thin sections. Let's see.
We have required qualifications: degree, years experience, proficiency, etc. Preferred qualifications list.
We also have "Why This Role Is Unique", "Ideal Candidate Profile". Could be part of Responsibilities or Qualifications? Might be better to include as part of Qualifications or maybe a separate section like "About the role" already covers.
We have Pay: "The anticipated base pay range for this position is $117,000 to $201,250." Also mention bonus etc. So we can include Pay section.
Benefits: list of benefits: medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, group legal insurance, consolidated retirement plan (pension) and savings plan (401(k)). Also time off benefits: vacation up to 120 hours, sick time up to 40 hours, holiday pay up to 13 days, personal/family time up to 40 hours. So we can include Benefits.
Schedule: Not mentioned besides location and no remote. No schedule details like full-time, hours. Not given. So we should not create Schedule section.
Skills: Could list preferred skills: Advanced Analytics, Agility Jumps, Coaching, Critical Thinking, Data Engineering, Data Governance, Data Modeling, Data Privacy Standards, Data Science, Digital Fluency, Execution Focus, Hybrid Clouds, Organizing, Presentation Design, Technical Development, Technical Writing, Technologically Savvy. Also required skills: Python, SQL, etc. We could include Skills section.
But we must avoid thin sections. If we include Skills, we need to ensure it's substantive. It is a list of skills; that's okay.
We also have location info: "This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ. (No remote option.)" Could be part of About the role or maybe a separate section like "Location". But location not in allowed headings; we could fold into About the role paragraph.
We must not include boilerplate like EEO, accommodation, etc. Remove those.
We must not repeat job title.
We must not include application instructions.
Now craft HTML.
Structure:
About the role
... paragraphs combining About The Opportunity, Why This Role Matters, Position Summary, maybe Ideal Candidate Profile? We'll keep concise but preserve all substantive info. Must not invent or summarize away detail. We need to preserve ALL substantive information; do NOT invent or summarize away detail. That means we cannot omit details; we must keep all info. However we can rephrase? The instruction says "Preserve ALL substantive information; do NOT invent or summarize away detail." So we cannot omit any substantive info. We must keep everything. That is tricky: we need to include all details from the posting, but we can format them into HTML. We cannot leave out any detail like years of experience, specific technologies, etc. So we need to include all bullet points, qualifications, responsibilities, benefits, pay, etc.
Thus we need to include all content in the HTML, just organized under headings.
Thus we need to include everything: About the opportunity, why role matters, position summary, key responsibilities (with sub-bullets), data stewardship, qualifications (required and preferred), why role is unique, ideal candidate profile, pay, benefits, location, maybe also note about no remote.
We must not include boilerplate like EEO, accommodation, working papers, etc. Also remove the lines about Johnson & Johnson belief statements at start? Those are generic but maybe considered boilerplate? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." The opening paragraph about health is everything, credo, etc. Might be considered boilerplate? It's not EEO but it's generic corporate description. Might be considered boilerplate? Not sure. It's not explicitly listed but could be considered navigation? I'd say it's part of company description, maybe we can keep? The instruction says remove boilerplate; but that opening paragraph is generic fluff. Might be considered boilerplate. Safer to remove it? However it's substantive about mission? It might be considered fluff but not required. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. If we remove that paragraph, we are removing information (though maybe not substantive for the job). It's ambiguous. I'd keep it as part of About the role? But it's not specific to the role. Might be considered boilerplate. I'd remove it to keep focus.
Similarly lines like "Learn more at jnj.com." maybe not needed.
We need to keep all substantive info about the role: responsibilities, qualifications, pay, benefits, location, etc.
Thus we will craft sections:
- About the role: include the opportunity description, why role matters, position summary, ideal candidate profile maybe.
- Responsibilities: list the key responsibilities bullet points (two sub-sections: Discovery Data Products & Integration, Data Stewardship & Organizational Impact). We'll flatten into list items.
- Qualifications: include required qualifications (degree, years experience, proficiency, etc.) and preferred qualifications (list). Could separate into Required and Preferred subheadings? Not allowed; only headings list. Could put under Qualifications and include both required and preferred as paragraphs/lists.
- Skills: list of preferred skills (the long list). Also maybe required skills (Python, SQL, etc.) but those are already in qualifications. We'll still include skills section for the list.
- Pay: include base pay range, bonus description.
- Benefits: include medical, dental, etc., retirement, time off.
- Location: maybe include as part of About the role or separate? Not in allowed headings; we could put under About the role.
We must not create Schedule.
Now ensure we don't repeat job title.
Now produce HTML fragment.
We need to be careful to not include any markdown fences.
Let's draft.
First, About the role paragraph(s). We'll combine:
"Johnson & Johnson Innovative Medicine is seeking a Principal Data Engineer dedicated to our Biologics Discovery organization. This is a high-leverage role responsible for shaping how discovery data is structured, connected, and made AI-ready across Biologics Discovery. The role serves as the bridge between scientific workflows, data consumers, and technology partners, ensuring that discovery data products support scientific research, analytics, machine learning, and agentic workflows. This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ. (No remote option.)
High-quality, well-governed scientific data is foundational to our vision for AI-enabled biologics discovery. This role provides senior technical leadership within Biologics Discovery, translating scientific needs into data products, scientific data models, and requirements, and working with enterprise data and technology partners to ensure discovery data is trusted, connected, and AI-ready as the broader ecosystem evolves.
As a Principal Data Engineer, you will lead the design of discovery data products, scientific data models (schemas, entities, and relationships), and integration requirements that enable discovery data to be acquired, connected, harmonized, and delivered across the Biologics Discovery ecosystem. You will work closely with scientists and AI/ML teams to translate their needs into durable, reusable, and AI-ready data assets. Working in close partnership with enterprise Data Strategy & Products and Technology teams, you will ensure discovery data needs are represented in enterprise standards and that those standards are effectively applied within Biologics Discovery. You will help shape the future-state discovery data ecosystem while delivering near-term value through trusted data products, harmonized data, and metadata practices that support long-term interoperability and reuse.
The ideal candidate is a technical leader and hands-on builder in a pharma or biotech organization. They combine strong data engineering expertise with the ability to influence across organizational boundaries. They can design scalable scientific data products, define data schemas, champion data standards, navigate complex stakeholder interactions, deliver high-impact solutions, and elevate the capabilities of technical teams through mentorship and example."
That seems to capture all substantive info from About The Opportunity, Why This Role Matters, Position Summary, Ideal Candidate Profile, and location.
Now Responsibilities: we need to list each bullet from Key Responsibilities and Data Stewardship & Organizational Impact.
Let's extract bullets:
Discovery Data Products & Integration:
- Design and deliver AI-ready discovery data products that support ML, AI, and insight generation across Biologics Discovery, applying agile delivery practices to respond to evolving scientific needs.
- Define and lead the delivery of scalable integration requirements, transformation patterns, and data schemas that support discovery data acquisition, harmonization, and downstream analytics, working with scientific, data, and technology stakeholders to enable reliable data exchange across systems.
- Translate scientific and analytical requirements from discovery teams into data product specifications, data contracts, acceptance criteria, and delivery requirements, in partnership with scientists, AI/ML teams, and technology partners.
- Define access and data consumption patterns that enable analytics, modeling, and agentic AI workflows, aligned with industry data standards and frameworks.
- Catalog discovery instruments, data types, and data sources to inform data product prioritization and sustainable integration approaches.
Data Stewardship & Organizational Impact:
- Establish, champion, and drive adoption of standards and best practices for discovery data products, including data quality, provenance, lineage, reproducibility, metadata, and documentation.
- Partner with ontology, data architecture, platform, and AI teams to ensure discovery data products are connected, discoverable, and suitable for advanced analytics, ML, and agentic AI applications.
- Apply FAIR data principles, so data products are reusable, scalable, and interoperable.
- Serve as a thought leader in scientific data architecture, harmonization, and AI-ready data practices across the organization.
We'll put these as
- items.
Now Qualifications: Required and Preferred.
Required:
- Degree in Computer Science, Data Science, Engineering, or a related computational field.
- 8+ years (Bachelor's), 5+ years (Master's), or 3+ years (Ph.D.) of experience designing and delivering data products, data models, and analytics-ready datasets within pharmaceutical, biotechnology, or life sciences organizations.
- Deep proficiency in Python and SQL, with hands-on experience designing and implementing reusable and scalable data assets on cloud data platforms (e.g., Snowflake, AWS, Azure, BigQuery) to support analytics, machine learning, and AI-driven workflows.
- Experience applying FAIR data principles, metadata management, controlled vocabularies, data lineage, and provenance practices in scientific data environments.
- Demonstrated technical leadership through architecture reviews, mentorship, code reviews, or leadership of complex technical initiatives.
- Proven ability to lead complex cross-functional technical initiatives and establish data standards across multiple stakeholder groups.
- Strong communication and the seniority to represent the team credibly in architecture, data, and strategy forums.
Preferred:
- Prior technical mentorship or leadership responsibilities.
- Experience with ontologies, semantic technologies, knowledge graphs, or ontology-driven data architectures.
- Experience in biologics discovery, high-throughput experimentation,
About the role
... paragraphs combining About The Opportunity, Why This Role Matters, Position Summary, maybe Ideal Candidate Profile? We'll keep concise but preserve all substantive info. Must not invent or summarize away detail. We need to preserve ALL substantive information; do NOT invent or summarize away detail. That means we cannot omit details; we must keep all info. However we can rephrase? The instruction says "Preserve ALL substantive information; do NOT invent or summarize away detail." So we cannot omit any substantive info. We must keep everything. That is tricky: we need to include all details from the posting, but we can format them into HTML. We cannot leave out any detail like years of experience, specific technologies, etc. So we need to include all bullet points, qualifications, responsibilities, benefits, pay, etc. Thus we need to include all content in the HTML, just organized under headings. Thus we need to include everything: About the opportunity, why role matters, position summary, key responsibilities (with sub-bullets), data stewardship, qualifications (required and preferred), why role is unique, ideal candidate profile, pay, benefits, location, maybe also note about no remote. We must not include boilerplate like EEO, accommodation, working papers, etc. Also remove the lines about Johnson & Johnson belief statements at start? Those are generic but maybe considered boilerplate? The instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." The opening paragraph about health is everything, credo, etc. Might be considered boilerplate? It's not EEO but it's generic corporate description. Might be considered boilerplate? Not sure. It's not explicitly listed but could be considered navigation? I'd say it's part of company description, maybe we can keep? The instruction says remove boilerplate; but that opening paragraph is generic fluff. Might be considered boilerplate. Safer to remove it? However it's substantive about mission? It might be considered fluff but not required. The instruction says preserve ALL substantive information; do NOT invent or summarize away detail. If we remove that paragraph, we are removing information (though maybe not substantive for the job). It's ambiguous. I'd keep it as part of About the role? But it's not specific to the role. Might be considered boilerplate. I'd remove it to keep focus. Similarly lines like "Learn more at jnj.com." maybe not needed. We need to keep all substantive info about the role: responsibilities, qualifications, pay, benefits, location, etc. Thus we will craft sections: - About the role: include the opportunity description, why role matters, position summary, ideal candidate profile maybe. - Responsibilities: list the key responsibilities bullet points (two sub-sections: Discovery Data Products & Integration, Data Stewardship & Organizational Impact). We'll flatten into list items. - Qualifications: include required qualifications (degree, years experience, proficiency, etc.) and preferred qualifications (list). Could separate into Required and Preferred subheadings? Not allowed; only headings list. Could put under Qualifications and include both required and preferred as paragraphs/lists. - Skills: list of preferred skills (the long list). Also maybe required skills (Python, SQL, etc.) but those are already in qualifications. We'll still include skills section for the list. - Pay: include base pay range, bonus description. - Benefits: include medical, dental, etc., retirement, time off. - Location: maybe include as part of About the role or separate? Not in allowed headings; we could put under About the role. We must not create Schedule. Now ensure we don't repeat job title. Now produce HTML fragment. We need to be careful to not include any markdown fences. Let's draft. First, About the role paragraph(s). We'll combine: "Johnson & Johnson Innovative Medicine is seeking a Principal Data Engineer dedicated to our Biologics Discovery organization. This is a high-leverage role responsible for shaping how discovery data is structured, connected, and made AI-ready across Biologics Discovery. The role serves as the bridge between scientific workflows, data consumers, and technology partners, ensuring that discovery data products support scientific research, analytics, machine learning, and agentic workflows. This position will be based at one of our office locations in either Spring House, PA (strongly preferred), Titusville, NJ, or Raritan, NJ. (No remote option.) High-quality, well-governed scientific data is foundational to our vision for AI-enabled biologics discovery. This role provides senior technical leadership within Biologics Discovery, translating scientific needs into data products, scientific data models, and requirements, and working with enterprise data and technology partners to ensure discovery data is trusted, connected, and AI-ready as the broader ecosystem evolves. As a Principal Data Engineer, you will lead the design of discovery data products, scientific data models (schemas, entities, and relationships), and integration requirements that enable discovery data to be acquired, connected, harmonized, and delivered across the Biologics Discovery ecosystem. You will work closely with scientists and AI/ML teams to translate their needs into durable, reusable, and AI-ready data assets. Working in close partnership with enterprise Data Strategy & Products and Technology teams, you will ensure discovery data needs are represented in enterprise standards and that those standards are effectively applied within Biologics Discovery. You will help shape the future-state discovery data ecosystem while delivering near-term value through trusted data products, harmonized data, and metadata practices that support long-term interoperability and reuse. The ideal candidate is a technical leader and hands-on builder in a pharma or biotech organization. They combine strong data engineering expertise with the ability to influence across organizational boundaries. They can design scalable scientific data products, define data schemas, champion data standards, navigate complex stakeholder interactions, deliver high-impact solutions, and elevate the capabilities of technical teams through mentorship and example." That seems to capture all substantive info from About The Opportunity, Why This Role Matters, Position Summary, Ideal Candidate Profile, and location. Now Responsibilities: we need to list each bullet from Key Responsibilities and Data Stewardship & Organizational Impact. Let's extract bullets: Discovery Data Products & Integration: - Design and deliver AI-ready discovery data products that support ML, AI, and insight generation across Biologics Discovery, applying agile delivery practices to respond to evolving scientific needs. - Define and lead the delivery of scalable integration requirements, transformation patterns, and data schemas that support discovery data acquisition, harmonization, and downstream analytics, working with scientific, data, and technology stakeholders to enable reliable data exchange across systems. - Translate scientific and analytical requirements from discovery teams into data product specifications, data contracts, acceptance criteria, and delivery requirements, in partnership with scientists, AI/ML teams, and technology partners. - Define access and data consumption patterns that enable analytics, modeling, and agentic AI workflows, aligned with industry data standards and frameworks. - Catalog discovery instruments, data types, and data sources to inform data product prioritization and sustainable integration approaches. Data Stewardship & Organizational Impact: - Establish, champion, and drive adoption of standards and best practices for discovery data products, including data quality, provenance, lineage, reproducibility, metadata, and documentation. - Partner with ontology, data architecture, platform, and AI teams to ensure discovery data products are connected, discoverable, and suitable for advanced analytics, ML, and agentic AI applications. - Apply FAIR data principles, so data products are reusable, scalable, and interoperable. - Serve as a thought leader in scientific data architecture, harmonization, and AI-ready data practices across the organization. We'll put these as
- items. Now Qualifications: Required and Preferred. Required: - Degree in Computer Science, Data Science, Engineering, or a related computational field. - 8+ years (Bachelor's), 5+ years (Master's), or 3+ years (Ph.D.) of experience designing and delivering data products, data models, and analytics-ready datasets within pharmaceutical, biotechnology, or life sciences organizations. - Deep proficiency in Python and SQL, with hands-on experience designing and implementing reusable and scalable data assets on cloud data platforms (e.g., Snowflake, AWS, Azure, BigQuery) to support analytics, machine learning, and AI-driven workflows. - Experience applying FAIR data principles, metadata management, controlled vocabularies, data lineage, and provenance practices in scientific data environments. - Demonstrated technical leadership through architecture reviews, mentorship, code reviews, or leadership of complex technical initiatives. - Proven ability to lead complex cross-functional technical initiatives and establish data standards across multiple stakeholder groups. - Strong communication and the seniority to represent the team credibly in architecture, data, and strategy forums. Preferred: - Prior technical mentorship or leadership responsibilities. - Experience with ontologies, semantic technologies, knowledge graphs, or ontology-driven data architectures. - Experience in biologics discovery, high-throughput experimentation,