Sr. Manager, Principal Data Architect

embecta•Parsippany, NJ

22h•Hybrid

About The Position

The Senior Manager, Principal Data Architect will define embecta’s enterprise data architecture, long-term vision, standards, and roadmap to support business growth and an AI-ready decision intelligence platform. This strategic, high-visibility role combines enterprise architectural leadership with hands-on contribution, spanning data platforms, governance, integration, analytics, and AI/ML enablement. The architect will advise Data & Analytics and business leadership on architectural tradeoffs and investment priorities while helping teams translate strategy into deliverable solutions. The role is hybrid, with on-site work three days a week at embecta’s Parsippany, New Jersey office. The position supports enterprise reporting, standard KPIs, AI/ML model training and deployment, conversational analytics, agentic applications, and migration from legacy warehouse and archive platforms to a lakehouse. It also helps shape a Hub & Spoke operating model through architectural guardrails, reusable data-product standards, and practices that balance enterprise consistency with domain agility.

Requirements

Bachelor’s degree in computer science, Information Systems, Engineering, Data Science, or a related field.
10+ years of experience in data architecture, data engineering, analytics architecture, or related enterprise data roles.
Demonstrated experience setting enterprise data architecture strategies and roadmaps and driving them through execution.
Experience designing secure, scalable enterprise data platforms in cloud environments.
Proven experience architecting for BI and AI/ML workloads at scale and making platform-level tradeoffs across competing demands.
Demonstrated ability to operate as both a strategic architect and execution leader, influencing delivery teams and contributing hands-on when necessary.
Deep knowledge of Azure data services, including Azure Data Lake and Azure Databricks or comparable cloud and lakehouse platforms, including cost, security, reliability, and scalability tradeoffs.
Strong expertise in SAP S/4HANA and SAP Datasphere; Databricks or modern lakehouse platforms; Power BI and enterprise analytics; data modeling; data integration architecture; data governance; and data quality.
Strong knowledge of Unity Catalog and enterprise governance integration patterns, including Microsoft Purview.
Ability to define reference architectures, reusable patterns, architectural standards, and design guardrails across federated teams.
Executive-level communication and influencing skills, including explaining recommendations, assumptions, risks, and tradeoffs to senior IT and business leaders.
Ability to work effectively in a Hub & Spoke or federated data organization and mentor architects and senior engineers.
Sound business judgment, a continuous-improvement mindset, and the ability to deliver results in a high-growth, entrepreneurial environment.

Nice To Haves

An advanced degree.
Experience in manufacturing, supply chain, life sciences, healthcare, or another regulated industry.

Responsibilities

Own the enterprise data architecture vision and multi-year roadmap to enable new business capabilities and AI/ML demands.
Translate strategy into executable architecture, partnering with engineering and business teams to remove delivery barriers and contributing hands-on when needed.
Set enterprise-wide data governance and security architecture standards using Unity Catalog, including target-state access control, lineage, and data quality patterns, and integration with Microsoft Purview for unified data discovery.
Serve as the senior technical authority for BI and AI/ML workloads, establishing platform decisions that support enterprise reporting, standard KPIs, model training and deployment, conversational analytics, and agentic applications.
Define enterprise data integration patterns and reference architectures that unify internal and external data sources and guide onboarding of new domains and data products.
Lead enterprise-scale migration from legacy data warehouse and archive platforms to the lakehouse, including architectural runway, sequencing, dependencies, and risk mitigation across workstreams.
Shape the Hub & Spoke operating model by defining architectural guardrails, reusable data-product standards, and practices that balance enterprise consistency with domain agility.
Architect scalable and secure patterns for orchestrating data and AI workflows using core Azure services.
Advise Data & Analytics and business leadership by translating priorities such as Commercial Forecasting, Cost-to-Serve, and Spend Intelligence into architectural investment decisions, risks, and tradeoffs.
Mentor senior data engineers and architects across the Hub and Spokes, review key designs, and raise architectural rigor before solutions scale.
Evaluate emerging data, AI/ML, analytics, and governance capabilities and make enterprise-level build-versus-buy and adopt-versus-wait recommendations.