Principal Data Architect

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 (individual contributor) 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.

In this senior individual contributor role, you will shape the enterprise data and AI strategy, advance 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.