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).
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.

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.
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.

Vacation –120 hours per calendar year
Sick time
40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time
up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52-week rolling period10 days
Volunteer Leave – 32 hours per calendar year
Military Spouse Time-Off – 80 hours per calendar year

Principal Data Engineer – Safety Analytics, Global Medical Safety

Key skills