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: https://www.mathworks.com/company/jobs/resources/applying-and-interviewing.html#onboarding.

We are seeking a highly seasoned Data Architect ( i ndividual c ontributor) with hands ‑ on experience spanning enterprise data architecture, modern data platforms, analytics, and AI-enabled solutions. The ideal candidate is a self ‑ driven, collaborative, and deeply technical leader who thrives on partnering with business and technology stakeholders to design and deliver enterprise ‑ scale data and AI solutions.

I n this senior individual contributor role, you will shape the enterprise data and AI strategy , ad vance core data capabilities, and lead the end ‑ to ‑ end execution of complex, cross ‑ functional initiatives. This position carries significant architectural ownership and influence, requiring someone who can think strategically while remaining highly engaged in technical execution .

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.