Principal Data Architect

Team: Business Applications and Tools

Location: US-MA-Natick

Salary Range: USD 224,000 - 140,000

Job Summary

MathWorks has a hybrid work model that enables staff members to split their time between office and home. The hybrid model provides the advantage of having both in-person time with colleagues and flexible at-home life optimizations. Learn More: mathworks.com/company/….

Are you energized by solving complex, meaningful problems with a highly collaborative team?

Are you passionate about shaping enterprise data architecture and AI strategies that drive real impact?

Do you want to work with products that make the world a better place?

If you answered yes, we want to speak with you. Seeking a seasoned Data Architect to help define and deliver enterprise-scale data and AI solutions. This hands-on individual contributor role spans enterprise data architecture, modern data platforms, advanced analytics, and AI-enabled solutions. The ideal candidate is a self-driven, collaborative technical leader who excels at translating business needs into scalable architectures and partnering across business and technology teams to turn strategy into impactful solutions.

MathWorks nurtures growth, appreciates inclusivity, encourages initiative, values teamwork, shares success, and rewards excellence.

Responsibilities

Enterprise Data Strategy & Architecture

• Define the target-state Data and AI architecture aligned with business goals and technology strategy.
• Establish architectural standards, principles, and best practices across the data ecosystem.
• Develop and maintain a multi-year roadmap covering data platforms, integrations, and AI enablement.
• Design scalable, secure, and interoperable data architectures that support enterprise analytics, operational reporting, and AI-driven use cases.

AI Data Readiness & AI Architecture

• Define enterprise AI data readiness strategies to ensure data is accessible, trusted, and AI-ready.
• Assess and improve data maturity across structured and unstructured data, including metadata, semantic models, lineage, and quality.
• Design scalable architecture and reusable patterns for AI, machine learning, advanced analytics, and automation.
• Collaborate with business, analytics, and AI teams to identify data gaps and prioritize improvements.
• Define architectural standards for RAG, vector databases, knowledge repositories, and enterprise AI platforms.
• Establish standards for metadata, observability, discoverability, and data products to maximize AI adoption and business value.
• Evaluate emerging AI technologies and recommend solutions that improve scalability, performance, and time-to-value.
• Incorporate responsible AI principles, including transparency, explainability, traceability, and auditability, into solution designs.

Stakeholder Collaboration & Leadership

• Partner closely with business and technical stakeholders to understand needs, define requirements, and co-create data solutions that deliver measurable outcomes.
• Translate complex technical concepts into clear, executive-ready communication.
• Provide architectural leadership and thought partnership across cross-functional teams.
• Strong ability to influence stakeholders and drive outcomes without formal authority.

Minimum Qualifications

• A bachelor's degree and 10 years of professional work experience (or equivalent experience) is required.
• Experience with data architecture

Additional Qualifications

• Experience in enterprise data architecture, data management, analytics platforms, and AI-enabled data ecosystems.
• Demonstrated success leading large, complex, cross-functional data initiatives.
• Strong ability to influence stakeholders and drive outcomes without formal authority.
• Analytical and structured thinker—comfortable navigating ambiguity and solving complex data problems.
• Highly self-directed, organized, and capable of independently driving strategic and technical workstreams.
• Deep understanding of AI data readiness principles, including metadata management, data products, semantic layers, data lineage, and AI-ready data architectures.
• Experience implementing data localization, data residency, or data sovereignty requirements across global environments.