We are seeking a Senior Staff Data Engineer looking to build the next generation of data pipelines and applications across the development of innovative new systems and solutions using a rapidly changing landscape of emerging technologies including generative AI and large language models. Working across the practices, techniques and tools used for the operational management of large language models in production environments – the Senior Staff Data Engineer role is proper for you if you're a subject matter expert in designing data integration frameworks and pipelines and still love to jump in and be "hands-on" when needed. This team is focused on proving the value of new tech and bringing it to production quickly.
In the Senior Staff Data Engineer role, you'll have the opportunity to partner with internal stakeholders, data engineers, visualization experts, data scientists, and other technologists across the businesses. You've come to the right place if you love to take large, disparate data sets and build them into flexible and scalable analytics applications and warehouses. Here, you can create the extraordinary. Join us. The ideal candidate should be well-versed in designing, building, and supporting APIs, machine learning services and frameworks, LLMs, lang-chain, and foundational data warehousing technologies. In addition, the candidate should be excited about the possibilities that Generative AI can be leveraged to accelerate various parts of the business.
Your primary focus will be building reliable, scalable, and efficient pipelines for use in LLMs and crafting our vision for LLM analytics. You will be essential in defining the team's strategy, evaluating, and integrating data patterns and technologies, and building pipelines alongside domain experts and data scientists.
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.
- Deep 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)
- 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.
- 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.
- Have worked well with data scientists, business analysts, and machine learning infrastructure to connect the dots between business and technology partners.
- Develop automated tests for your code, ensuring every function, service, and object is compatible with your team's work and with the many systems within the NBCUniversal system portfolio and cross-device and browser compatibility.
- Create documentation for developers and business users to help them understand our products.
- Work collaboratively with a multidisciplinary team within a matrixed organization, leveraging strong interpersonal skills to navigate system complexities and deploy solutions efficiently.
- Deploy to cloud-based platforms and troubleshoot application, cloud, and configuration issues when necessary.
- Utilize tools for code & test generation to dramatically accelerate the delivery of features and components you create.