• Provide support and maintenance for Information Systems and system integrations • Serve as a resource on projects for implementing Information Systems or developing system integrations

• Design, develop, and maintain large-scale data pipelines using Scala, Spark, and cloud-native technologies • Solve complex data engineering challenges by designing efficient, resilient distributed systems • Contribute to production-grade data platforms, delivering high-quality, tested code • Ensure software quality through clean, testable, and well-documented code • Collaborate within agile value stream teams, working closely with engineers, product owners, and cross-functional stakeholders • Provide technical guidance to peers, contributing to architecture discussions and promoting best engineering practices • Support release and operational processes, including deployments, monitoring, alerting, and continuous improvement of system reliability • Drive improvements in performance, cost efficiency, and scalability of data processing workloads

• Support the development of scalable data pipelines and platform components, following established frameworks, standards, and guidance from senior engineers. • Apply software engineering best practices, including coding standards, version control, testing, and documentation, to deliver reliable and maintainable code. • Assist in testing and release activities by executing test cases, validating changes, and following deployment procedures to ensure quality and stability. • Collaborate with engineers, product owners, and cross-functional teams in an agile environment to support feature development and delivery commitments. • Contribute to operational activities by following monitoring practices, deployment standards, and incident protocols to maintain platform reliability. • Continuously develop technical skills through hands-on work, mentorship, and feedback, increasing autonomy and contribution over time.

• Lead the build of GenAI-powered product experiences and shared AI platform infrastructure • Design, build, and operate production AI features • Build shared AI platform layer including retrieval infrastructure and eval frameworks • Write LLM applications and integrations with marketing platforms and BI tools • Evaluate model and feature quality using structured eval frameworks • Use AI-driven SDLC tooling for AI and non-AI code • Coordinate with the Personalization team • Document AI system design decisions and operational lessons • Own the observability of AI systems built including latency, cost, quality drift, and error rates