Data Architect with AI experience(Hybrid)

Position:Senior Data Architect

Location:NJ

Duration:Long term

Job Description:

We are seeking an experienced Senior Data Architect to support the evolution and operation of a centralized MedTech Supply Chain analytics data repository that serves as the foundation for enterprise reporting, analytics, AI, and data products.

This role will be responsible for defining and implementing data architecture standards, designing scalable data models, establishing data management best practices, and creating repeatable ways of working that enable consistent delivery across multiple business domains. The ideal candidate combines deep technical expertise with strong collaboration skills and can work effectively with business stakeholders, data product owners, data engineers, analytics teams, and governance organizations.

Key Responsibilities

Data Architecture & Solution Design

Define and maintain the target-state architecture for the Digital Brain (a central data repository).

Establish architecture principles, standards, patterns, and reference designs for analytics data assets.

Conduct architecture reviews to ensure alignment with enterprise data strategies and technology standards.

Partner with data engineering teams to design scalable, high-performing analytics solutions.

Evaluate architectural impacts of new business requirements, data sources, and analytics use cases.

Data Modeling

Design and maintain conceptual, logical, and physical data models for analytics and reporting use cases.

Leverage dimensional, data vault, and other analytical modeling approaches as appropriate.

Ensure consistent representation of business concepts across data products and analytical domains.

Create and maintain data lineage, metadata, and model documentation.

Support the evolution of semantic models that enable consistent business reporting and self-service analytics.

Data Standards & Governance

Define and maintain enterprise data standards, naming conventions, modeling standards, and design guidelines.

Establish reusable design patterns and best practices for data ingestion, transformation, storage, and consumption.

Collaborate with Data Governance, Data Quality, and Metadata Management teams to improve data consistency and trust.

Support implementation of master and reference data standards across analytics assets.

Drive adherence to approved data architecture standards and governance requirements.

Ways of Working & Operating Model Development

Establish repeatable processes and best practices for data architecture and analytics solution delivery.

Define architecture engagement processes, design review procedures, and documentation standards.

Create templates, playbooks, and frameworks for data product design and implementation.

Support intake, prioritization, and solution assessment activities for new requests.

Promote a product-oriented and reusable approach to analytics asset development.

Stakeholder Engagement

Partner with business leaders, analysts, engineers, and solution architects to understand data requirements.

Facilitate architecture workshops and design sessions.

Translate business needs into scalable and sustainable data architecture solutions.

Provide architecture guidance and mentorship to project teams and junior data modelers.

Required Qualifications

8+ years of experience in Data Architecture, Data Warehousing, or Analytics Architecture roles.

Strong expertise in enterprise data modeling methodologies, including dimensional modeling and modern analytics data architectures.

Experience designing and implementing centralized analytics platforms, data warehouses, or lakehouse environments.

Deep understanding of data governance, metadata management, data quality, and data lifecycle management.

Experience developing enterprise data standards and architecture frameworks.

Demonstrated ability to work across business and technical teams.

Strong communication and presentation skills with the ability to engage both technical and business stakeholders.

Preferred Qualifications

Experience with modern cloud analytics platforms such as Databricks, Snowflake, Azure, AWS, or Google Cloud.

Experience supporting semantic layer technologies and enterprise BI platforms.

Familiarity with data product operating models and domain-oriented data ownership concepts.

Experience with data catalog and metadata management solutions (e.g., Alation, Collibra, Purview).

Knowledge of data quality frameworks and monitoring practices.

Experience supporting AI, advanced analytics, and self-service reporting initiatives.