Lead Engineer, Evidence Management

Johnson & Johnson

Confirmed live yesterday High trust
Closes in 7 days Hybrid

Quick summary

Work type
Hybrid
Location
Raritan, NJLimerick, Ireland
Salary
$102,000–$175,950 / yr
Posted
8 days ago
Freshness
Confirmed live yesterday
Closes
Sep 18, 2026 (soon)

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $203k
This role $139k
$86k most similar roles pay here $251k

This role pays less than 96% of similar roles. Most pay $174,600–$230,425 — the shaded band above. At the midpoint, this role pays about $139k versus about $203k for comparable roles.

Based on 240 similar postings.

Employer

About Johnson & Johnson

Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices

Johnson & Johnson currently has 46 open roles on FindRole.

Listed pay typically runs $117,000–$201,250 across 42 roles with salary data.

Most-posted roles

View all roles at Johnson & Johnson

At a glance

TL;DR · Lead Engineer, Evidence Management

Lead Engineer, Evidence Management serves as a technical leader within the Digital Ecosystem team, focusing on engineering leadership, application architecture, and delivery governance for evidence management systems spanning Scientific Affairs and Regulatory Affairs. The role involves designing scalable, compliant solutions for regulatory planning, submissions, case management, and process automation while leading AI-enabled modernization through Generative AI, Machine Learning, and NLP. You will manage the technical roadmap for data flows, integration patterns, and user experience across various evidence platforms. Essential skills include extensive expertise in the Appian platform, including solution design, process modeling, and infrastructure governance. The position requires proficiency in SAFe delivery, cloud-native architectures, and managing complex requirements within highly regulated environments involving GxP and SOX compliance to ensure data integrity and auditability for critical scientific and regulatory evidence management workflows.

What you'll do

  • Design and maintain the application architecture strategy and engineering roadmap for evidence management across Regulatory and Scientific Affairs.
  • Develop scalable, compliant, and interoperable solutions using the Appian platform to manage data flows, workflows, and integrations.
  • Translate complex regulatory and scientific processes into technical designs, data models, and actionable delivery backlogs.
  • Lead the integration of AI capabilities, including Generative AI and machine learning, while ensuring safety and compliance in regulated environments.
  • Define data architecture principles for evidence metadata, content management, and analytics-ready data products.
  • Establish engineering standards for APIs, event-driven integrations, and automated workflow orchestration.
  • Ensure all technical solutions comply with GxP, SOX, and other regulatory requirements regarding data integrity and cybersecurity.
  • Act as the primary technical advisor for Appian-based decisions, infrastructure, and evidence management capabilities.

What we're looking for

  • Bachelor degree in Computer Science, Engineering, Information Systems, Data Science, AI/ML, or a related field.
  • 10+ years of experience in software, application, platform, data, or enterprise technology architecture and delivery.
  • Mandatory hands-on experience engineering, architecting, and delivering solutions on the Appian platform.
  • Demonstrated expertise in Appian solution design including process modeling, integrations, data models, and role-based security.
  • Experience leading application engineering delivery in Agile or SAFe environments.
  • Demonstrated experience architecting AI-enabled enterprise solutions involving Generative AI and Machine Learning.
  • Hands-on knowledge of LLMs, prompt engineering, vector databases, and responsible AI controls.
  • Working knowledge of GxP, SOX, privacy, cybersecurity, and regulated SDLC practices in life sciences.

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