• Share ownership and evolve the end-to-end data architecture across streaming, batch, and analytical workloads • Design data models that preserve signal from unstructured and semi-structured sources • Enable machine learning workflows by defining clear contracts between raw, derived, and model-ready data • Establish standards for schema evolution, dataset versioning, reproducibility, and data quality • Partner closely with engineering teams to improve scalability, reliability, and cost efficiency • Embed applied data science considerations into architectural decisions, including feature reuse, temporal consistency, and leakage prevention • Document and communicate architectural decisions clearly across teams**

• Define and evolve enterprise data architecture principles, standards, and reference architectures aligned to business priorities and modern practices • Lead the design and maintenance of enterprise conceptual, logical, and physical data structures, ensuring consistent definitions and shared semantics • Establish domain-oriented architecture patterns for reusable, governed data products and interoperable data flows • Develop architecture patterns supporting both batch and streaming workloads, including event-driven approaches and canonical representations • Partner with data engineering teams to translate architecture and logical designs into scalable physical implementations on AWS cloud data platforms • Ensure consistent data definitions, lineage, and metadata documentation across domains by enabling cataloging and lineage platforms • Maintain a central repository of architecture artifacts, definitions, and metadata documentation • Influence data design decisions across programs and projects through architecture reviews and standards enforcement • Facilitate architecture and data design workshops with business and technical stakeholders • Lead, mentor, and develop a team of data architects and specialists, ensuring consistent delivery of architecture outcomes • Stay current on evolving architecture trends and evaluate their applicability to Deckers

• Partner with the Director, Data Architecture to implement enterprise data architecture standards and ensure consistent adoption across domains and initiatives • Lead architecture design for assigned domains and initiatives, including conceptual, logical, and physical data structures aligned to business requirements and data products • Define and maintain domain-aligned data definitions and semantic consistency to support a unified understanding of key business entities and metrics • Develop architecture patterns that support batch and streaming use cases, including event-driven data flows and canonical representations • Collaborate with data engineering teams to translate architecture designs into scalable physical implementations on AWS cloud data platforms • Participate in architecture and design reviews, providing guidance, feedback, and enforcement of standards for new data products and platform changes • Partner with governance teams to align architecture with data quality, privacy, security, access controls, classification, retention, and metadata requirements • Maintain architecture artifacts and documentation in a central repository, including definitions, reference designs, and decision records to improve reuse and consistency • Facilitate workshops with business and technical stakeholders to capture requirements, refine definitions, and align on domain boundaries and ownership • Identify architecture gaps and improvement opportunities, and recommend practical enhancements to standards, patterns, and tooling that improve scalability and interoperability • Coach and enable engineers and analysts on architecture expectations and best practices through working sessions, office hours, and documentation

• Maintain and troubleshoot frontline data tools, primarily Airtable-based systems supporting Response Operations • Build and maintain integrations between response systems using REST APIs, ensuring data flows reliably across platforms • Provide responsive technical support during activations, including outside standard business hours when needed • Monitor system health and proactively address issues before they impact field operations • Refine and evolve the response data model to better reflect operational realities and reduce complexity in cooperation with key stakeholders • Develop and improve Airtable templates and offline data tools that standardize processes while remaining flexible for diverse response contexts • Reduce redundant data entry by connecting response unit processes and automating data flows where possible • Build automated data QA processes to support data integrity and enable reliable real-time reporting • Document data structures, integrations, and templates to enable knowledge transfer and long-term maintainability • Partner with Enterprise Technology to ensure engineering work follows organizational standards and best practices • Collaborate closely with Response Technology team members to understand operational needs and translate those into technical solutions • Work with field teams to gather feedback and iterate on tools based on real-world usage • Participate in code reviews and maintain clean, well-documented codejectives.