As part of the global Operations & Technology organization, the D&A team is focused on data and analytics strategies for the future. We support NBCU’s vast portfolio of brands - from broadcast, cable, news, and sports networks to film studios, world-renowned theme parks, and a diverse suite of digital properties. We take pride in supplying our business groups with data to advise and shape strategic business decisions related to our content. We're looking for a passionate problem solver who’s looking to build the next generation of data pipelines and applications to support our generative AI initiatives. Working across one or more of our main subject areas – research, marketing, engineering frameworks – the Sr Data Engineer role is right for you if you’re a “hands-on” coder who can build and cleanse large datasets in order to report out actionable insights. In addition, you’ll be working with internal stakeholders, data engineers, visualization experts, data scientists, and other technologists across the business. If you’re someone who loves to take large, disparate data sets and build them into flexible and scalable analytics applications and databases, you’ve come to the right place. Here you can create the extraordinary. Join us! Responsibilities: Design, build, and scale data pipelines across a variety of source systems and streams (internal, third-party, and cloud-based), distributed/elastic environments, and downstream applications and self-service solutions. Solid understanding of data modeling, warehousing, and architecture principles. Implement appropriate design patterns while optimizing performance, cost, security, and scale and end-user experience. Collaborate with cross-functional teams to understand data requirements and develop efficient data acquisition and integration strategies. Interface with other technology teams to extract, load, and transform data from a wide variety of data sources using cloud-native data engineering principles. Become a subject matter expert for data engineering-related technologies and designs. Strong understanding of Machine Learning best practices (e.g., training/serving, feature engineering, feature/model selection, imbalance data, RAG patterns) and algorithms (e.g., deep learnings, optimization) Coach and guide others within the organization to build scalable pipelines based on foundational data engineering principles. Participate in development sprints, demos, and retrospectives alongside releases and deployment. Build and manage relationships with supporting engineering teams to deliver work products to production effectively. Collaborate with business leaders, engineers, and product managers to understand data needs. Create documentation for developers and business users to help them understand our products. Implement the appropriate design patterns while optimizing performance, cost, security, and scale and end user experience Collaborate with business leaders, engineers, and product managers to understand data needs. Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using cloud-native data engineering principles