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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Associate Director, Safety Data Solutions Architect - **Company:** Johnson & Johnson - **Location:** Horsham, PA, United States - **Experience:** Experienced - **Salary:** $137,000.0 - $235,750.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, BigQuery, Cloud Computing, Cloud Storage, Continuous Integration, Data Flow Control, Key Management, Metadata, Performance Tuning, Role-Based Access Control, Zero Trust Network Access, Data Streaming, Management of Software Versions, Policy as Code, Feature Store, Feature Engineering, Retrieval-Augmented Generation, Togaf, Event Driven Architecture, Information Technology, Machine Learning Operations, CIS Benchmarks, Terraform, Data Pipelines, GXP, Microservices - **Published:** October 27, 2026 - **Apply:** https://www.careers.jnj.com/en/jobs/r-105206/associate-director-safety-data-solutions-architect/ ## About the Role * 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 for cloud analytics workloads. ## Description * 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.