You're a frontend-focused engineer who enjoys working on structured content systems and large-scale migrations within enterprise ecommerce environments. You have experience translating legacy CMS content into modern composable architectures and enjoy building scalable content models, reusable components, and performant storefront experiences. You’re comfortable working within defined templates and strict scope constraints, and you understand how to balance technical execution with SEO parity and content integrity during migrations. You collaborate effectively with designers, strategists, and developers to ensure content renders correctly across environments. This role will support a high-priority initiative to migrate hundreds of marketing and store location pages off Adobe Experience Manager (AEM) into a modern Shopify + Sanity architecture within a defined 12-week delivery window. - Implement structured content schemas and document types within Sanity Studio - Configure marketing page templates, reusable components, and structured content modules - Build and maintain preview functionality and publishing workflows for content editors - Develop frontend page templates aligned to defined layout patterns - Build reusable UI components using modern React patterns - Fetch and render content from Sanity APIs into the storefront experience - Integrate Shopify Storefront API where product data is referenced within content pages - Support migration of approximately 260 marketing pages and 600 templated store detail pages - Write or support transformation scripts to convert AEM content structures into Sanity-compatible documents - Assist with bulk content imports and validate data integrity post-migration - Collaborate with delivery, UX, and engineering teams to maintain strict scope control and timelines

• Design and maintain scalable batch data pipelines in AWS to power analytics and ML use cases. • Develop and optimize SQL transformations and analytical datasets for BI and predictive workloads. • Build reliable ETL/ELT processes with monitoring and data quality checks. • Create feature-ready datasets and support feature engineering pipelines for ML initiatives. • Deliver production-grade data to support elasticity modeling and advanced performance analytics. • Design data infrastructure for A/B testing and measurable experimentation. • Develop ingestion pipelines for marketing and campaign analytics. • Contribute to CI/CD-driven MLOps workflows for model deployment and monitoring in AWS. • Collaborate on data governance, cost optimization, and scalable architecture decisions. • Enable integration of AI and LLM-powered capabilities through robust, future-ready data services.

• Create and manage ETL processes using Azure Data Factory to automate data flow between various systems and services • Implement data storage solutions using Azure Data Lake and Cosmos DB, ensuring high availability and scalability • Design and optimize data processes in Databricks, implementing Spark jobs that utilize Delta Lake for efficient data handling and versioning • Build and maintain analytics models that can harness the capabilities of Delta Lake for better data quality and performance • Develop scripts in Python and PowerShell to automate operational tasks, deployments, and monitoring of data workflows • Collaborate with development teams to integrate CI/CD practices using Azure DevOps and Git for version control • Manage and optimize MS SQL Server databases, writing efficient T-SQL queries for data manipulation and reporting • Ensure data integrity and security through proper database management practices • Implement logging and monitoring solutions to proactively address any issues impacting data applications and workflows • Conduct root cause analysis on data-related problems and develop solutions to mitigate future occurrences • Collaborate with business intelligence teams to create and share reports using Power BI • Provide insights and recommendations based on data analysis to guide business decisions

• Design, build, and own scalable, resilient data pipelines and data products • Power the core of Content Finance planning and systems • Answer questions regarding content budgets for upcoming forecasting seasons • Define modern data engineering practices • Establish strong data foundations and data modeling • Influence how GenAI is leveraged in the data engineering ecosystem • Shape decision-making on funding and content business growth