• Build and launch URM’s Snowflake-based enterprise data warehouse, including scalable ingestion pipelines, transformation frameworks, semantic data models, performance optimization, and robust security controls. • Establish a modern enterprise analytics ecosystem, including a governed BI layer, standardized KPIs, executive dashboards, and self-service analytics capabilities that empower data-driven decision making. • Design and operationalize a scalable enterprise integration platform (Boomi, MuleSoft, or similar) connecting ERP, WMS, POS, store platforms, vendor systems, and member-facing solutions. • Define and implement enterprise data governance, including data ownership and stewardship models, data quality standards, master data strategy, and lifecycle management controls. • Develop and maintain enterprise data standards, including a comprehensive data dictionary, canonical data models, and reusable integration patterns to ensure consistency and interoperability. • Shape URM’s enterprise AI strategy and architecture, including data readiness standards, model governance frameworks, responsible AI policies, and initial high-value AI use cases. • Serve as the enterprise architecture authority for ERP and WMS modernization initiatives, ensuring solutions are scalable, resilient, and maintainable. • Recruit, build, and lead high-performing hybrid teams across onshore and offshore data, integration, and AI engineering disciplines. • Implement enterprise architecture governance, including architecture review boards, technical standards, and design oversight across major initiatives. • Establish a Development Center of Excellence (CoE) to standardize development practices, engineering methodologies, and delivery approaches across URM. • Develop a reusable catalog of applications, data products, and services that enable innovation and value creation for URM and its member store owners.

• Influence Product Planning Roadmap: Influence product planning through informative impact sizing, enabling more strategic decision-making for Owner. • Uncover Product Gaps, and Recommend/Action Resolutions: Analyze the root cause for product success metric gaps and issues, and recommend immediate resolution pathways, or action them yourself. • Own Your Product Pod’s Analytics: Improve the analytics and engagement strategy for your product pod(s). This isn't just an insights role - you'll design and ship data products and automated AI workflows that enable teams to act on data without waiting for analysis. • Tackle emerging priorities: Lead or contribute to ad hoc projects as business needs arise; this role flexes with what matters most. • Ultimately, help grow our customers’ sales and improve their experience: Across all product pods, the mission is uniform: improve our customer’s experience and help customers grow their sales with Owner.com.

• Develop, train, and validate predictive and prescriptive models using Machine Learning algorithms and statistical techniques; • Perform exploratory data analysis (EDA) to identify patterns, opportunities, and trends; • Build and maintain structured, scalable data pipelines; • Clean, preprocess, and prepare large volumes of data from multiple sources; • Build and monitor performance metrics to evaluate deployed models; • Conduct statistical studies, simulations, and hypothesis testing to solve business problems; • Document processes, models, and methodologies developed; • Contribute to the continuous improvement of data policies, standards, governance, and data quality.

• Design, build, and deploy machine learning and AI-driven models to forecast revenue, optimize marketing and sales performance, and identify financial trends • Perform statistical modeling, feature engineering, and hypothesis testing to uncover key business drivers • Evaluate model performance through A/B testing, backtesting, and performance monitoring • Partner with the Data Engineering team to ensure scalable data pipelines and robust data quality using Snowflake, Rivery, and dbt • Develop reusable analytical frameworks, automated pipelines, and reproducible modeling processes • Maintain documentation and follow data governance best practices • Build dashboards and predictive analytics in Domo to surface insights for executives and business stakeholders • Collaborate with Product, Finance, Marketing, and Operations to translate business questions into measurable, data-driven outcomes • Support the VP of Data Engineering in building and operationalizing a high-performing data science environment • Research and prototype new AI and ML techniques (e.g., LLMs, generative AI, anomaly detection) • Stay up to date with the latest in machine learning, data science, and cloud analytics tools to enhance the company's capabilities