Associate Director, Safety Data Solutions Architect

Johnson & Johnson Innovative Medicine•Horsham, PA

1d•$137,000 - $235,750•Hybrid

About The Position

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). The role provides solution architecture leadership for pharmacovigilance and safety analytics, translating safety science needs into validated, scalable, and compliant data and AI capabilities. It partners with Safety Analytics Data Scientists on LLM and generative AI use cases, establishing platform guardrails, retrieval and inference patterns, and compliance controls. The architect coordinates Principal-level data engineers and cross-functional teams to deliver trusted, inspection-ready data products for analytics and AI services, emphasizing architecture strategy, regulatory readiness, responsible AI, enterprise alignment, cost efficiency, and operating excellence. Selective hands-on work includes reference implementations and reusable templates. The position shapes multi-year roadmaps and target and transitional architectures across data ingestion, transformation, storage, serving, and consumption on GCP. It establishes governed data and AI lifecycle practices, domain-aligned data products, canonical models, data contracts, integration standards, and controls for security, privacy, quality, observability, reliability, compliance, and cost. It chairs design reviews, coordinates engineering priorities and dependencies, mentors teams, and communicates with technical and non-technical stakeholders. The role collaborates with Safety Analytics Data Scientists and MLOps, while scientific ownership of model behavior and prompt design remains with the Data Scientists.

Requirements

10 or more years in data/analytics engineering and architecture, including at least 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 machine learning, covering 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 generative AI patterns for analytics, including feature engineering, vector stores, semantic retrieval, and RAG, with emphasis on explainability and controls; able to partner with Safety Analytics 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 other 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 is a plus.
Certifications such as Google Professional Cloud Architect, Google Professional Data Engineer, TOGAF, or SAFe.
FinOps experience, including budget management and efficiency tracking for cloud analytics workloads.

Responsibilities

Establish baselines, performance indicators, and benefit-realization measures for data onboarding speed, solution reuse, platform reliability, inspection readiness, cost efficiency, and time to insight.
Partner with Safety, Product, Data Science, and Technology leaders to create 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 domain-aligned pharmacovigilance data products and curated datasets for signal detection, case analytics, and predictive safety, with quality, reliability, and inspection-readiness criteria.
Own target-state and transitional architectures for GCP data ingestion, transformation, storage, serving, and consumption; drive reuse through reference architectures and patterns.
Set guardrails for AI/ML and generative AI integrations, including embeddings, vector stores, semantic retrieval, and RAG, emphasizing explainability, traceability, and responsible AI.
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, least privilege, data minimization, PII/PHI protections, and distinct R&D and production pathways.
Define GCP standards for APIs, microservices, and retrieval services; establish reliability and security baselines, including SLO/SLA targets, error budgets, zero trust, secrets management, and telemetry.
Promote event-driven and streaming architectures with contract-first integration, backward-compatible evolution, and robust monitoring.
Partner with Safety Analytics Data Scientists and MLOps to define contract-first batch and online model and inference interfaces, including rate limits, cost controls, telemetry, and audit logging.
Govern CI/CD for data pipelines and analytics and retrieval services; embed policy-as-code, testing, artifact integrity, and promotion gates; manage cloud cost governance, 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, 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.

Benefits

Consolidated retirement plan (pension) and savings plan (401(k)), subject to plan terms.
Eligibility to participate in the long-term incentive program.
Vacation: 120 hours per calendar year, subject to policy and date of hire.
Sick time: 40 hours per calendar year, with different amounts for certain state residents, subject to policy and date of hire.
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.