Data Engineering Manager

The Data Engineering Manager will be a hands-on data engineering role supporting Universal+, Hayu, and the wider NBCUniversal International Networks & Direct-to-Consumer business, with a particular focus on local delivery and stakeholder needs in Florida and Latin America. Reporting to the Senior Director, Data Platform, this role will build and maintain reliable data solutions across Snowflake, cloud data platforms, ETL/ELT pipelines, integrations, orchestration, and reporting environments. The role will also coordinate local data initiatives, manage dependencies and delivery plans, and act as a key bridge between business stakeholders, product teams, analytics teams, vendors, and the wider International Data Platform team. Responsibilities: Hands-on Data Engineering: Design, build, test, deploy, and support reliable data pipelines, data models, integrations, and reporting datasets across Snowflake and related cloud data platform services. Local Data Delivery: Support local business and product priorities by translating stakeholder requirements into practical, maintainable data engineering solutions. Snowflake & Data Platform Support: Develop and maintain Snowflake objects, SQL transformations, data models, secure access patterns, performance tuning, and cost-conscious platform usage. Integrations & Automation: Build and support integrations between the data platform and internal or third-party systems, including analytics, CRM, marketing technology, partner platforms, finance systems, content systems, and operational tooling. Project Coordination: Coordinate project plans, priorities, dependencies and stakeholder updates for local and regional data initiatives. Stakeholder Management: Act as a local point of contact for product, analytics, finance, marketing, operations, commercial, content, and technology stakeholders, ensuring requirements, priorities, risks, and delivery status are clearly understood. Data Quality, Governance & Support: Monitor and improve data quality, pipeline reliability, documentation, access controls, privacy, security, lineage, and operational support processes. Team Collaboration: Work closely with data engineers, analysts, product owners, engineering teams, vendors, and enterprise technology partners to deliver well-documented and maintainable solutions. Continuous Improvement: Identify opportunities to simplify processes, automate manual workflows, improve data quality, reduce operational effort, and unlock new datasets that support reporting, insight, activation, AI, and governance use cases.