Associate Director, Safety Data Solutions Architect

Role Overview

Johnson & Johnson Global Medical Safety (GMS) is seeking an Associate Director, Safety Data Solutions Architect to lead the target architecture, standards, and delivery roadmap for safety analytics on Google Cloud Platform (GCP). This role provides solution architecture leadership for pharmacovigilance and safety analytics, translating safety science needs into validated, scalable, and compliant data and AI capabilities. Partner with Safety Analytics (SA) Data Scientists on LLM/GenAI use cases to ensure platform guardrails, retrieval/inference patterns, and compliance controls. The Architect will coordinate Principal-level data engineers and cross-functional teams to deliver trusted, inspection-ready data products for downstream analytics and AI services.

Key Responsibilities

• Translate architecture strategy into measurable outcomes by establishing baselines, performance indicators, and benefit-realization measures for data onboarding speed, solution reuse, platform reliability, inspection readiness, cost efficiency, and time to insight for safety scientists.
• Partner with Safety, Product, Data Science, and Technology leaders to translate business capabilities and scientific workflows into a multi-year data and platform roadmap, including build-versus-buy recommendations, investment sequencing, adoption and value measures, and the rationalization and retirement of legacy technologies.
• Establish governed AI lifecycle patterns and platform controls for the evaluation, approval, versioning, deployment, monitoring, and reproducibility of AI-enabled solutions, including defined human oversight, provider portability, rollback mechanisms, and controlled use of models, prompts, retrieval sources, and agent tools.
• Define domain-aligned data products and curated datasets for pharmacovigilance (signal detection, case analytics, predictive safety) with clear quality, reliability, and inspection-readiness criteria.
• Own target-state and transitional architectures across ingestion, transformation, storage, serving, and consumption on GCP (e.g., BigQuery, Dataform, Cloud Storage); drive reuse via reference architectures and patterns.
• Set guardrails for AI/ML and GenAI integrations (embeddings, vector stores, semantic retrieval, RAG) with emphasis on explainability, traceability, and responsible AI.
• Establish canonical data models, data contracts, and lifecycle practices; implement contract-first integration, schema versioning, and controlled interface evolution across producers/consumers.
• Chair design reviews; approve solution designs and enforce non-functional requirements (resilience, scalability, security, observability, SLOs); remove delivery blockers and ensure quality and compliance.
• Drive data quality, governance, and observability, including validation standards, lineage/metadata, privacy and security controls (least privilege, data minimization, PII/PHI protections), and clear R&D vs. production pathways.
• Define standards for APIs, microservices, and retrieval services on GCP (e.g., Cloud Run, GKE); establish reliability and security baselines (SLO/SLA targets, error budgets, zero trust, secrets management, telemetry).
• Promote event-driven and streaming architectures (e.g., Pub/Sub, Dataflow) with contract-first integration, backward-compatible evolution, and robust monitoring.
• Partner with SA Data Scientists and MLOps to define contract-first model and inference interfaces (batch/online), including rate limits, cost controls, telemetry, and audit logging.
• Govern CI/CD for data pipelines and analytics/retrieval services; embed policy-as-code, testing, artifact integrity, and promotion gates; own cost governance (budgets, showback/chargeback, efficiency targets, right-sizing, usage policies).
• Coordinate Principal-level engineers' scope and priorities across pipelines, services, feature stores, and AI/ML integrations; manage cross-team dependencies; communicate decisions to technical and non-technical audiences; mentor teams and uplift standards.
• Hands-on (selective): Deliver and maintain reference implementations and design spikes (e.g., ingestion to BigQuery, contract-first schemas, Cloud Run service scaffolds, Pub/Sub/Dataflow patterns) to de-risk delivery and accelerate adoption.

Required Qualifications

• 10+ years in data/analytics engineering and architecture, including 3+ years in solution/enterprise architecture roles.
• Demonstrated leadership coordinating Principal/Senior engineers and cross-functional teams in regulated environments.
• Deep architecture experience on GCP: BigQuery, Dataform, Cloud Storage; orchestration and automation; familiarity with Pub/Sub, Dataflow; services on Cloud Run and/or GKE.
• Strong data modeling for analytics and ML (batch and streaming), performance optimization, and observability practices.
• Proven experience implementing data contracts, validation, schema versioning, lineage/metadata, and access control.
• Hands-on understanding of AI/ML and GenAI patterns for analytics (feature engineering, vector stores, semantic retrieval, RAG) with emphasis on explainability and controls.
• GxP/CSV/CSA experience for data and analytics platforms; audit readiness and change control.
• Excellent communication and stakeholder management; ability to simplify complex topics and influence senior leaders.

Preferred Qualifications

• Master's degree in Computer Science, Engineering, or related field (or equivalent experience).
• Experience in pharmaceuticals, life sciences, healthcare, or related regulated domains.
• Experience with Terraform (Infrastructure as Code) and CI/CD; policy-as-code.
• Exposure to feature store enablement and MLOps on GCP; familiarity with Vertex AI a plus.
• Certifications: Google Professional Cloud Architect or Professional Data Engineer; TOGAF; SAFe.
• FinOps experience, including budget management and efficiency tracking for cloud analytics workloads.