Data Engineer II – Data & Analytics IT • GDD Data Product - Safety & Regulatory

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Position: Data Engineer II – Data & Analytics IT • GDD Data Product - Safety & Regulatory

Location: LVL, New Jersey, US

Position Summary: As a Data Engineer II, you will help build and operate reliable, secure, and user-centric data and analytics capabilities for Global Drug Development (GDD) across Safety and Regulatory data products. You will design and optimize cloud-native data pipelines, dimensional models, and self-service analytics to support Pharmacovigilance (PV) scientists, Regulatory specialists, and cross-functional stakeholders. You will also leverage GenAI and semantic search techniques (e.g., RAG, vector embeddings) to improve data discovery, compliance automation, and decision support. This role is ideal for an engineer with 3–5 years of experience who thrives in regulated environments, can reverse-engineer complex systems, and enjoys turning ambiguous requirements into scalable, production-grade solutions.

Key Responsibilities:

• Contribute to cross-functional data and AI initiatives across WWPS and Regulatory product lines; collaborate closely with PV scientists, Regulatory leads, data product owners, and engineering teams to deliver high-impact outcomes.

• Design, build, and optimize scalable ETL/ELT pipelines and data models using AWS-native services (e.g., S3, Redshift) and Spark for large, complex life-sciences datasets; implement robust orchestration (e.g., cron or similar) and monitoring.

• Develop and maintain dimensional models and data marts; define clear source-to-target mappings, data lineage, and documentation to support auditability, validation, and reuse.

• Migrate and modernize data pipelines (e.g., Postgres to Redshift), reducing refresh latency and improving availability, performance, and cost efficiency through techniques like partitioning, distribution/sort keys, and caching.

• Architect and deliver GenAI/NLP-powered features for data discovery and compliance automation using RAG, vector embeddings (e.g., FAISS), and frameworks like LangChain with OpenAI/Anthropic/Llama.

• Build self-service analytics and interactive dashboards (Tableau, QuickSight, Power BI) to support operational and regulatory decision-making (e.g., query forecasting, submission tracking, safety signal exploration).

• Ingest and harmonize data from multiple clinical programs into S3-backed data lakes; implement Spark transforms and Redshift models to expand safety and adverse event data domains.

• Partner with PV and QA teams to plan and execute functional/regression/validation testing; document test evidence and support GxP-aligned processes to ensure high-quality releases.

• Drive PoCs for governance and analytics, evaluate emerging patterns, and translate learnings into scalable platform capabilities.

• Contribute to engineering standards, code reviews, and documentation; collaborate with onshore/offshore teams and mentor interns/junior analysts on best practices and business alignment.

• Stay current on trends in GenAI, RAG, vector databases, semantic search, and cloud data engineering; propose and integrate best practices for continuous platform improvement.

Qualifications & Experience:

• 3–5 years of hands-on experience in Data Engineering/Analytics delivering production-grade data pipelines, models, and analytics in a cloud environment (AWS preferred) within regulated or life-sciences settings (highly preferred).

• Bachelor’s degree in Engineering or a Scientific discipline required; Master’s degree in Analytics or related field preferred.

• Proficiency in Python and SQL with experience in Spark