Principal Data Engineer – Safety Analytics (Global Medical Safety)

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

Prefered 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 contro