Associate Director, Safety Data Solutions Architect

Johnson & Johnson•Titusville, PA

10d•Hybrid

About The Position

Johnson & Johnson Global Medical Safety (GMS) seeks an Associate Director, Safety Data Solutions Architect to lead the target architecture, standards, and delivery roadmap for safety analytics on Google Cloud Platform (GCP). The role provides solution architecture leadership for pharmacovigilance and safety analytics, translating safety science needs into validated, scalable, compliant data and AI capabilities. It partners with Safety Analytics (SA) Data Scientists on LLM and GenAI use cases to establish platform guardrails, retrieval and inference patterns, and compliance controls. The position coordinates Principal-level data engineers and cross-functional teams to deliver trusted, inspection-ready data products for analytics and AI services. The role emphasizes architecture strategy, regulatory readiness, responsible AI, alignment with enterprise patterns, cost efficiency, and operating excellence. Selective hands-on work includes building reference implementations and “golden-path” templates that accelerate adoption. It is based in Horsham, Pennsylvania, or Titusville, New Jersey, and is identified as hybrid in the posting.

Requirements

10+ years in data/analytics engineering and architecture, including 3+ years in solution or enterprise architecture roles.
Demonstrated leadership coordinating Principal/Senior engineers and cross-functional teams in regulated environments.
Deep GCP architecture experience with BigQuery, Dataform, and Cloud Storage; orchestration and automation; and familiarity with Pub/Sub, Dataflow, and services on Cloud Run and/or GKE.
Strong data modeling for analytics and ML across 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, including feature engineering, vector stores, semantic retrieval, and RAG, with emphasis on explainability and controls; able to partner with SA Data Scientists without owning model development.
GxP/CSV/CSA experience for data and analytics platforms, including audit readiness and change control.
Excellent communication and stakeholder-management skills, with the ability to simplify complex topics and influence senior leaders.

Nice To Haves

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

Responsibilities

Translate architecture strategy into measurable outcomes by establishing baselines, performance indicators, and benefit-realization measures for onboarding speed, reuse, reliability, inspection readiness, cost efficiency, and time to insight.
Partner with Safety, Product, Data Science, and Technology leaders to develop a multi-year data and platform roadmap, including build-versus-buy recommendations, investment sequencing, adoption and value measures, and legacy technology rationalization and retirement.
Establish governed AI lifecycle patterns and platform controls for evaluation, approval, versioning, deployment, monitoring, and reproducibility, including human oversight, provider portability, rollback mechanisms, and controlled use of models, prompts, retrieval sources, and agent tools.
Define pharmacovigilance data products and curated datasets for signal detection, case analytics, and 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; drive reuse through reference architectures and patterns.
Set guardrails for AI/ML and GenAI integrations, including embeddings, vector stores, semantic retrieval, and RAG, emphasizing explainability, traceability, and responsible AI while partnering with SA Data Scientists without taking over model development.
Establish canonical data models, data contracts, and lifecycle practices; implement contract-first integration, schema versioning, and controlled interface evolution.
Chair design reviews, approve solution designs, enforce non-functional requirements, remove delivery blockers, and ensure quality and compliance.
Drive data quality, governance, and observability, including validation standards, lineage and metadata, privacy and security controls, and distinct R&D and production pathways.
Define standards for APIs, microservices, and retrieval services on GCP, and establish reliability and security baselines such as SLO/SLA targets, error budgets, zero trust, secrets management, and telemetry.
Promote event-driven and streaming architectures using technologies such as Pub/Sub and Dataflow, with contract-first integration, backward-compatible evolution, and robust monitoring.
Partner with SA Data Scientists and MLOps to define contract-first batch and online model and inference interfaces, 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 and retrieval services, embedding policy-as-code, testing, artifact integrity, and promotion gates; own cost governance, including budgets, showback/chargeback, efficiency targets, right-sizing, and 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.
Selectively deliver and maintain reference implementations and design spikes, such as ingestion to BigQuery, contract-first schemas, Cloud Run service scaffolds, and Pub/Sub/Dataflow patterns, to reduce delivery risk and accelerate adoption.

Benefits

Consolidated retirement plan (pension) and savings plan (401(k)), subject to the terms of the plans.
Eligibility to participate in the long-term incentive program.
Vacation: 120 hours per calendar year, subject to applicable policies and date of hire.
Sick time: 40 hours per calendar year; different amounts apply to employees residing in Colorado or Washington.
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, or foster care of a child.
Bereavement leave: 240 hours for an immediate family member and 40 hours for an extended family member per calendar year.
Caregiver leave: 80 hours in a 52-week rolling period.
Volunteer leave: 32 hours per calendar year.
Military spouse time-off: 80 hours per calendar year.