Senior Enterprise Data Modeler/Architect

Our purpose is design for the good of humankind. Being a part of MillerKnoll means being a part of something larger than your work team, or even your brand. At MillerKnoll, the Enterprise Data Architect is accountable for defining and governing the enterprise data strategy and architecture to directly enable business growth, operational efficiency, and digital transformation. This role establishes a cohesive, scalable data ecosystem that ensures data is trusted, accessible, and actionable across all business domains. As a senior architecture leader, you will standardize how data is defined, mastered, integrated, and consumed-eliminating fragmentation and enabling a consistent enterprise-wide data foundation. You will partner closely with business, product, engineering, and analytics teams to align data architecture with strategic priorities while reducing complexity and cost. Essential Functions of the Enterprise Data Architect at MillerKnoll You'll have opportunities to speak up, solve problems, lead others, and be an owner every day as someone who does the following... Enterprise Data Strategy & Architecture Define and evolve the enterprise data strategy aligned to business objectives and digital initiatives. Establish reference architectures and target-state data patterns supporting operational and analytical use cases. Ensure data architecture supports scalability, flexibility, and cost efficiency across cloud and legacy environments. Canonical Data Model & Common Language Own the definition and governance of the Enterprise Canonical Data Model (CDM) and Global Data Dictionary. Customer, Product, Asset) across all systems and domains. Drive adoption of a unified data language to eliminate semantic inconsistencies across teams. Master Data & "Single Source of Truth" (SSOT) Define authoritative systems of record for all critical data domains. Architect and govern Master Data Management (MDM) strategies, including Golden Record creation and stewardship models. Reduce redundancy and conflicting data definitions across platforms. Data Integration & Orchestration Establish and enforce standardized integration patterns (API-first, event-driven, and data streaming). Govern enterprise data flows to ensure consistency, traceability, and scalability. Drive alignment toward modern distributed data architectures (e.g., Data Mesh principles where appropriate). Data Performance, Latency & Cost Optimization Define standards for balancing real-time operational needs with analytical processing requirements. Ensure data platforms are optimized for both transaction processing and advanced analytics without excessive cost. Guide architecture decisions for high-performance systems and enterprise analytics platforms. Data Governance & Quality Establish and enforce enterprise data standards, policies, and governance frameworks. Actively participate in domain data governance boards to ensure accountability and adoption. Improve data quality, lineage, and transparency across the enterprise. Business Alignment & Value Realization Translate complex business requirements into scalable, high-impact data solutions. Partner with executive leadership to support strategic decision-making through data. Ensure data architecture investments deliver measurable business value. Enterprise Architecture Leadership Serve as a key member of the Architecture Review Board (ARB), governing adherence to enterprise data standards. Lead data architecture reviews across all major initiatives and product domains. Drive enterprise-wide reduction of data and technology redundancy ("integration and data debt"). Partner with Information Security to align data architecture with security, privacy, and compliance requirements. Contribute to enterprise-wide architectural direction, ensuring data is a first-class concern in all solutions. Operating Model & Ways of Working Promote a federated, domain-oriented data ownership model with strong central governance. Enable hypothesis-driven data engineering, encouraging experimentation and iterative delivery. Apply a customer and business outcome lens to all data architecture decisions. Continuously evaluate and adopt modern data technologies and practices where they provide clear value. Skills & Capabilities Technical & Architectural Expertise Deep expertise in distributed data systems across hybrid (legacy + cloud-native) environments. Strong understanding of data modeling, data integration patterns, and modern data platforms. Experience with AWS and Snowflake. Strategic & Business Acumen Ability to connect data architecture decisions to business outcomes and financial impact. Strong cost-awareness when designing scalable data solutions. Qualifications Bachelor's degree in Computer Science, Information Systems, Business, or related field is preferred. 10+ years of experience in enterprise data, architecture, or related IT disciplines is preferred. Experience operating at or advising executive leadership levels. MillerKnoll is comprised of people of all abilities, gender identities and expressions, ages, ethnicities, sexual orientations, veterans from every branch of military service, and more. We're committed to equal opportunity employment, including veterans and people with disabilities. In general, MillerKnoll positions are closed within 45 days and are open for applications for a minimum of 5 days. We encourage our prospective candidates to submit their application(s) expediently so as not to miss out on our opportunities. MillerKnoll complies with applicable disability laws and makes reasonable accommodations for applicants and employees with disabilities. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact MillerKnoll Talent Acquisition at Type: Full Time

Created: 2026-05-09