Senior Data Architect – Enterprise Data Strategy

Job Requisition ID # 26WD96409 Position Overview Autodesk is seeking a Senior Data Architect to join our Data Architecture team, which is responsible for defining and evolving the long-term data architecture vision. This role operates at the strategic layer of architecture — shaping how data is structured, governed, activated, and leveraged across operational systems (Sales, Marketing, Support, Finance), customer-facing platforms (digital experience, subscriptions, personalization), and analytics and AI/ML ecosystems. While this is a strategy-defining role, it is not purely conceptual. The ideal candidate is deeply technical and hands-on — capable of whiteboarding system flows, refining logical data models, reviewing integration designs, and mentoring engineers and architects to ensure the strategy is grounded in implementation reality. A critical dimension of this role is the ability to proactively identify opportunities to unlock enterprise data for AI-driven workflows — enabling automation, intelligent decisioning, personalization, and new product capabilities through well-architected, trusted data foundations. You will partner with senior enterprise and solution architects to ensure Autodesk’s data ecosystem evolves cohesively and scales to support new business models, customer experiences, and AI-powered innovation. Key Responsibilities Establish and evolve the enterprise data architecture vision aligned to Autodesk’s business strategy and digital transformation goals Define enterprise data domains and ownership models across operational, customer, and analytical ecosystems Develop guiding principles and guardrails for how data is created, shared, mastered, governed, and activated Shape architectural patterns that support data products, domain-oriented ownership, and modern data paradigms Identify enterprise data assets that can be activated to power AI/ML, personalization, automation, and intelligent decisioning Design architectural patterns that make high-quality, governed data accessible for AI workflows across the organization Ensure machine learning pipelines and AI systems are grounded in authoritative enterprise data domains Partner with AI/ML and product teams to define scalable, reusable data foundations for experimentation and production AI systems Remove systemic barriers that limit data discoverability, accessibility, and reuse Create and maintain high-level conceptual and logical data models spanning CRM, ERP, Finance, subscription systems, marketing platforms, support systems, and analytics environments Design and validate end-to-end data flows that traverse multiple enterprise systems Identify architectural fragmentation and define modernization strategies Ensure operational and analytical architectures are cohesive and mutually reinforcing Engage directly in whiteboarding sessions, integration design reviews, and model refinement with engineering teams Guide translation of conceptual and logical models into implementable physical designs Review APIs, event schemas, data contracts, and pipeline architectures to ensure alignment with enterprise standards Provide practical implementation guidance for AI/ML, personalization, and real-time data use cases Lead by example with architectural artifacts that engineering teams can operationalize Partner with business, product, and engineering leaders to align data capabilities with business priorities Collaborate with enterprise and domain architects to align roadmaps and architectural decisions Influence funding and roadmap decisions before delivery begins Facilitate architectural governance processes and design reviews Establish scalable approaches for data management and authoritative data sources Define patterns for data contracts, lineage, observability, and quality validation Ensure architecture supports privacy, compliance, security, and financial controls Embed governance into architecture without creating unnecessary process o ... (truncated, view full listing at source)