Principal Data Engineer – Safety Analytics

Jobtailor

New Jersey

On-site

USD 140,000 - 190,000

Full time

14 days+

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Job summary

Jobtailor in New Jersey seeks a senior Data Engineer to design and maintain production-grade data pipelines supporting pharmacovigilance analytics. You will enable AI/GenAI workflows and ensure data outputs are reproducible, explainable, and compliant for safety decision support.

You will own the end-to-end data lifecycle, drive architecture on GCP (BigQuery, Dataform, Cloud Run, GKE), and implement data quality, contracts, and governance. Collaboration with safety scientists and IT is essential.

Qualifications

5+ years of data/analytics engineering experience with increasing responsibilities.
Proficient in Python and SQL; strong data architecture for analytics/ML.
Experience with data contracts, validation, schema versioning, and governance.
Experience building APIs/microservices and cloud concepts (GCP preferred).
Excellent written and verbal communication and Agile/SDLC knowledge.

Responsibilities

Design and maintain production-grade data pipelines and curated datasets.
Ensure reproducible, explainable analytics outputs for safety decisions.
Enable AI/GenAI workflows for safety analytics and RAG patterns.
Own end-to-end data lifecycle from intake to serving and analysis.
Lead architecture across ingestion, transformation, storage, serving on GCP.
Define data quality, lineage, observability, and governance standards.
Collaborate with safety scientists, epidemiologists, IT, and platform teams.

Skills

Python Programming

SQL Programming

Data Governance

Communication Skills

Teamwork

Education

Master's degree in Computer Science or Engineering

Tools

BigQuery

Dataform

Cloud Run

GKE

Jenkins

Terraform

Job description

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.
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.
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.
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.
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.
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.

Requirements

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, and teamwork skills to foster a collaborative team environment.
Solid understanding of SDLC and Agile methodologies, alongside basic project management skills.
Experience building production workloads on Google Cloud Platform (GCP) is preferred.
Experience provisioning infrastructure using Terraform (Infrastructure as Code) and building CI/CD pipelines (e.g., Jenkins) is preferred.
Experience in pharmaceuticals, life sciences, healthcare, or a related regulated domain is preferred.
GCP certification is preferred.
Experience enabling AI/ML and GenAI workflows (e.g., feature engineering, RAG patterns, semantic retrieval) for analytical applications is preferred.

Core Competencies

Demonstrates expertise in designing and maintaining data pipelines and architectures for safety analytics, ensuring compliance with Global Medical Safety requirements. Proficient in implementing data governance practices and enabling AI/ML workflows to support regulatory reporting and decision-making.

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