Senior Data Engineer, Data Management

NBCUniversal is one of the world's leading media and entertainment companies. We create world-class content, which we distribute across our portfolio of film, television, and streaming, and bring to life through our global theme park destinations, consumer products, and experiences. We own and operate leading entertainment and news brands, including NBC, NBC News, NBC Sports, Telemundo, NBC Local Stations, Bravo, and Peacock, our premium ad-supported streaming service. We produce and distribute premier filmed entertainment and programming through our powerhouse film and television studios, including Universal Pictures, DreamWorks Animation, and Focus Features, and the four global television studios under the Universal Studio Group banner, and operate industry-leading theme parks and experiences around the world through Universal Destinations & Experiences, including Universal Orlando Resort, home to Universal Epic Universe, and Universal Studios Hollywood. NBCUniversal is a subsidiary of Comcast Corporation. Visit for more information.

Our impact is rooted in improving the communities where our employees, customers, and audiences live and work. We have a rich tradition of giving back and ensuring our employees have the opportunity to serve their communities. We champion an inclusive culture and strive to attract and develop a talented workforce to create and deliver a wide range of content reflecting our world.

Job Description

The Senior Data Engineer, Data Mgmt and BI will design, build, and support scalable data solutions that enable trusted analytics, operational reporting, and data-driven decision making across the organization. This role will partner closely with product, analytics, engineering, and business stakeholders to translate ambiguous business needs into reliable technical solutions, while helping establish strong engineering standards across data pipelines, cloud platforms, data models, APIs, automation, and observability. The ideal candidate is a hands-on engineer who can work independently, mentor others, communicate clearly with technical and non-technical partners, and continuously improve how data is delivered, governed, tested, and supported in a fast-moving environment. 

• Design, build, test, deploy, and maintain scalable data pipelines that ingest, transform, validate, and publish structured and semi-structured data from internal and external sources. 
• Translate business and product requirements into technical designs, data models, integration patterns, and execution plans that support reliable analytics and operational workflows. 
• Develop reusable, maintainable code using modern data engineering practices, with an emphasis on performance, testability, observability, and long-term supportability. 
• Build and optimize batch, event-driven, and serverless data processing patterns using cloud-native services and distributed processing frameworks. 
• Contribute to architecture decisions across data lake, warehouse, orchestration, metadata, API, and application integration patterns. 
• Implement data quality checks, monitoring, alerting, and operational runbooks to ensure pipelines are accurate, reliable, and supportable in production. 
• Design and maintain CI/CD pipelines, automated testing, deployment workflows, and infrastructure-as-code patterns that improve delivery speed and reduce operational risk. 
• Partner with analytics, BI, product, and business teams to enable timely, trusted, and well-documented data products and reporting solutions. 
• Evaluate source systems, APIs, files, data contracts, and transformation logic to identify risks, dependencies, data gaps, and opportunities for simplification. 
• Participate in Agile delivery practices including sprint planning, backlog refinement, code reviews, release planning, incident response, and retrospectives. 
• Mentor engineers, promote engineering best practices, and help raise the quality bar for code, documentation, testing, and production readiness. 
• Create and maintain clear technical documentation, architecture diagrams, data lineage notes, implementation guides, and support materials as systems evolve. 

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Analytics, or a related field, or equivalent practical experience. 
• 5+ years of hands-on data engineering experience designing, building, testing, deploying, and supporting production data pipelines. 
• Strong experience with data modeling, data architectur