• Architect and maintain scalable runtime production environments across cloud-native and containerized systems, including transforming applications into Kubernetes-friendly formats for bare-metal deployment • Own the technical roadmap for the platform vertical, identifying gaps, planning initiatives, and aligning engineering effort to mission objectives • Provide mentorship to engineers across the team, fostering professional development and raising the technical bar • Identify technical overlap across projects and guide teams on leveraging each other for efficient delivery • Work closely with Mission Managers, providing insights on engineering competency, capacity, velocity, and alignment with mission objectives • Collaborate with product teams, providing feedback on product fit and contributing to product direction based on production usage • Work with customers to define technical roadmaps that align to mission objectives • Advocate for and enforce DevSecOps practices across the platform layer, including security automation, container security, and compliance • Evaluate and select optimal methods for solving complex business problems, assessing new products and their potential impact on mission objectives • Drive continuous improvement in CI/CD pipelines, deployment patterns, and operational tooling

• Design, build, test, and operate distributed backend services and APIs that support high-volume customer interactions. • Take end-to-end ownership of platform work, from framing ambiguous problems and shaping technical plans through implementation, rollout, and production support. • Improve reliability, scalability, latency, and cost across shared platform systems that serve teams across the business. • Partner closely with Product, SRE, and peer engineering teams to deliver platform improvements without destabilizing live traffic. • Participate in on-call, incident response, troubleshooting, and continuous improvement of alerting, runbooks, deployment quality, and capacity planning. • Raise engineering standards through code reviews, design reviews, pairing, documentation, and mentorship for other engineers on the team.

• Lead the design and architecture of scalable full-stack solutions aligned with product requirements • Own the architecture of core subscription and membership systems, including billing correctness, lifecycle state management, plan changes, cancellations, and workflow orchestration across commerce, pharmacy, and fulfillment • Partner closely with engineering teams across commerce, clinical care, pharmacy, and fulfillment to drive cross-functional technical initiatives and ensure platform decisions scale across the organization • Provide mentorship on best practices, design patterns, and coding standards • Advocate for team technical needs within the broader engineering organization • Participate in product discovery and validation through prototypes and experiments • Partner with product and design to ensure platform capabilities support an intuitive end-to-end customer experience • Drive strategic technical decisions to improve security, scalability, and performance • Break down complex projects into actionable tasks • Establish and enforce consistent coding and testing standards • Initiate efforts to reduce technical debt and enhance workflow efficiency • Implement tools and processes for monitoring, incident response, and performance optimization • Implement analytics and monitoring to demonstrate how engineering solutions directly impact key customer activation and reactivation KPIs • Analyze metrics and user feedback to continuously improve the platform • Maintain thorough technical documentation • Conduct code reviews and facilitate knowledge-sharing initiatives

• Build and optimize data pipelines (batch and streaming). • Extracting, analyzing and modeling rich and diverse datasets of structured and unstructured data. • Design software that is easily testable and maintainable. • Support in setting data strategies and our vision. • Keep track of emerging technologies and trends in the Data Engineering world, incorporating modern tooling and best practices at Craft. • Work on extendable data processing systems that allows to add and scale pipelines. • Apply machine learning techniques such as anomaly detection, clustering, regression classification, and summarization to extract value from our data sets. • Leverage AI-powered development tools (e.g. Cursor) to accelerate development, refactoring, and code generation.