Associate Director, Safety Data Solutions Architect

Job description

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 Science 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 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. The role emphasizes architecture strategy, regulatory readiness, and responsible AI, while ensuring alignment to enterprise patterns, cost efficiency, and operating excellence. A selective hands-on component focuses on reference implementations and “golden-path” templates that accelerate team adoption. 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; maintain clear accountability boundaries between platform governance and scientific ownership of model behavior. 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; partner with SA Data Scientists without overlapping model development. 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; SA Data Scientists retain ownership of prompt design and model behavior. 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 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; partner with SA Data Scientists without owning model development. 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 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