Principal Data Engineer - Safety Analytics (Global Medical Safety)

Johnson & Johnson Innovation; New Jersey; On-site; USD 170,000 - 230,000; Full time

Job summary

Johnson & Johnson Innovation is seeking a Principal Data Engineer to lead safety analytics data engineering on Google Cloud Platform (GCP). You will own end-to-end data lifecycle, data quality, and governance for pharmacovigilance analytics, enabling AI/ML workflows and secure, scalable data services.

You’ll collaborate with safety scientists, analytics teams, IT, and platform partners to deliver trusted analytics capabilities, while ensuring regulatory readiness and auditability across

Qualifications

• Master’s degree in Computer Science, Engineering, or closely related field.
• 5+ years of data engineering or analytics engineering experience.
• Proficient in Python and SQL with data pipelines and ML/AI workflows.

Responsibilities

• Design and maintain production-grade data pipelines for pharmacovigilance analytics.
• Lead data architecture decisions across ingestion, storage, and serving on GCP.
• Enforce data quality, governance, and compliance for safety analytics.
• Build APIs and microservices to operationalize analytics and ML capabilities.

Skills

Python

SQL

Data pipelines

Cloud architecture

GxP & Regulatory

Education

Master’s degree in CS/Engineering

Tools

GCP

BigQuery

Dataform

Terraform

Jenkins

GKE

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:

Data Analytics & Computational Sciences

Job Sub Function:

Data Engineering

Job Category:

Scientific/Technology

All Job Posting Locations:

Horsham, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine

Preferred Location:

Horsham, PA or Titusville, NJ. Remote work will considered on a case by case basis.

Role Overview

We are seeking a Principal Data Engineer to provide technical leadership within Global Medical Safety (GMS), supporting the Safety Analytics organization. This role is focused on building and enabling modern safety analytics tools using AI, Machine Learning, and GenAI, underpinned by robust, compliant, and scalable data engineering on Google Cloud Platform (GCP).

The Principal Data Engineer is responsible for end-to-end ownership of safety analytics data engineering, spanning data intake, data quality and continuity, pipeline and architecture design, automation, performance optimization, and compliance. The role enables advanced analytical, machine learning, and predictive capabilities for pharmacovigilance and serves as a technical data engineering leader within Global Medical Safety.

This is a Principal-level individual contributor role with broad technical influence, working closely with safety scientists, analytics teams, data scientists, IT, and platform partners to deliver trusted, production-grade analytics capabilities for safety decision-making.

Key Responsibilities

Safety Analytics & Pharmacovigilance Enablement

• Design and maintain production-grade data pipelines and curated datasets that directly support pharmacovigilance activities, including safety monitoring, analytics, and regulatory reporting.

• Ensure data engineering solutions produce reproducible, explainable, and trusted analytics outputs suitable for safety decision support and inspection readiness.

• Enable AI/ML and GenAI workflows for safety analytics, including:

• Feature engineering and feature store enablement
• Embeddings, vectorized representations, and semantic retrieval
• Retrieval-Augmented Generation (RAG) patterns for safety analytics tools

End-to-End Data Architecture & Lifecycle Ownership

• Own the end-to-end data lifecycle for safety analytics, from source system intake through transformation, serving, and downstream analytical consumption, ensuring data continuity, traceability, and integrity.

• Lead architectural decisions across ingestion, transformation, storage, and serving layers on GCP (e.g., BigQuery, Dataform, object storage).

• Design, implement, and automate scalable, reusable data pipelines and architectures to support evolving safety analytics needs.

Data Quality, Governance & Compliance

• Establish and enforce data quality, validation, lineage, and observability standards for safety analytics datasets.

• Define and implement data governance practices, including data contracts, schema versioning, access control, stewardship, and lifecycle management.

• Ensure safety analytics data and systems meet Global Medical Safety requirements for reliability, auditability, and regulatory use.

GxP Validation & Regulatory Readiness

• Apply GxP validation expertise to data pipelines, analytics services, and supporting infrastructure.

• Partner with quality and compliance teams to implement CSV/CSA-aligned controls, audit trails, documentation, and organizational change.

• Balance delivery velocity and innovation with the rigor required for regulated pharmacovigilance systems.

Services, APIs & Microservices

• Design and build APIs and microservices-based architectures to operationalize safety analytics and ML capabilities (e.g., feature serving, retrieval services, analytics backends).

• Deploy and operate services on GCP (e.g., Cloud Run, GKE) with a strong focus on security, scalability, and observability.

• Enforce contract-first integration patterns between producing and consuming systems to ensure reliability and safe evolution.

Infrastructure, CI/CD & Cost Optimization

• Provision and manage cloud infrastructure using Terraform (Infrastructure as Code) on GCP.

• Build and maintain CI/CD pipelines (e.g., Jenkins) for data pipelines, analytics services, feature pipelines, and ML data assets.

• Continuously optimize the performance and cost efficiency of data and analytics infrastructure while maintaining compliance and reliability standards.

Technical Leadership & Stakeholder Engagement

• Serve as a technical authority and data engineering leader for Safety Analytics within Global Medical Safety.

• Review and influence designs across pipelines, services, feature stores, and AI/ML integrations to maintain a high technical bar. Collaborate closely with safety scientists, epidemiologists, biostatisticians, analytics teams, IT, and platform partners to translate safety needs into scalable technical solutions.

• Communicate complex technical concepts and tradeoffs clearly to both technical and non-technical stakeholders.

• Enable and upskill teams through mentorship, guidance, and knowledge sharing on modern data, cloud, and AI technologies.

Qualifications

• Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience) is required.

• 5+ years of experience in data engineering or analytics engineering with increasing responsibilities.

• Proficient programming skills in Python and SQL.

• Deep understanding of data architecture for analytics and ML (e.g., batch/streaming, modeling, performance optimization).

• Proven ability to translate complex problems into clear, concise, and testable programming code/tools.

• Experience implementing data contracts, data validation, schema versioning, and governance practices, as well as a solid understanding of leading cloud concepts (GCP preferred).

• Experience designing and operating APIs and microservices-based architectures.

• Excellent written and verbal communication, customer service, interpersonal