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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, Data Engineering - **Company:** Universal Music Group. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $122,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Batch Processing, Big Data, Program Optimization, Customer Data Management, Data Architecture, Data Control, Information Engineering, Data Governance, Data Infrastructure, Data Mart, Digital Assets, Dimensional Modeling, Graph Database, Machine Learning, Data Streaming, Enterprise Data Management, Cloud Platform System, Feature Engineering, System Availability, Large Language Models, Snowflake, Apache Spark, Data Layers, Data Analytics, Data Management, Data Pipelines, Automation Anywhere, Databricks - **Published:** August 6, 2026 - **Apply:** https://umusic.wd5.myworkdayjobs.com/UMGUS/job/USA-CA-SMO-2220-Colorado-Ave/Senior-Manager--Data-Engineering_UMG-27136 ## About the Role * 10+ years of data engineering experience, with 3+ years in a leadership role and direct ownership of ecommerce, digital commerce, DTC, retail, or marketplace data domains. * Deep knowledge of modern data architectures (lakehouse, real-time streaming, batch processing). * Strong understanding of data modeling, including dimensional modeling, feature engineering, and semantic layer design. * Proven leadership experience delivering AI-ready data platforms and ML/AI workflows, including feature stores, training datasets, model data pipelines, personalization, demand forecasting, churn/retention, attribution, and other generative AI initiatives. * Experience driving data quality, governance, and observability frameworks critical for AI trust and explainability. * Demonstrated ability to translate business, ecommerce, analytics, and AI requirements into scalable data engineering solutions. * Strategic exposure to AdTech and MarTech, with experience in the music/entertainment industry preferred, and the ability to connect audience engagement, marketing technology, and data-driven growth initiatives * Experience with cloud ecosystems (AWS, Azure, or GCP) and big data technologies (Spark, Snowflake, Databricks, etc.). GCP is a strong plus. * Strong leadership, stakeholder management, and cross-functional collaboration skills. * Awareness of data governance and privacy principles, including customer data privacy, is preferred. * Ability to lead effectively in a fast-paced environment, balancing priorities while maintaining quality, stakeholder alignment, and delivery discipline. * Ability to lead the creation of impactful executive presentations that communicate strategy, tradeoffs, roadmap decisions, and measurable business outcomes., The actual base salary offered depends on a variety of factors, which may include, as applicable, the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job. All candidates are encouraged to apply. ## Description We are seeking an experienced and driven Senior Data Engineering Manager - Enterprise Data Products within the Global Data & Analytics team. You are passionate about leading teams that deliver scalable, reliable, and AI-ready data platforms that power enterprise decision-making, advanced analytics, and ecommerce growth. You understand that modern data platforms must not only support reporting, but also enable machine learning, generative AI, and intelligent applications through high-quality, well-governed, semantically consistent enterprise data. In this role, you will lead teams of data engineers responsible for delivering domain-specific data products, semantic layers, and data marts that serve as trusted, AI-ready sources of truth across the organization, while shaping how data is structured, governed, and exposed to support AI/ML use cases, feature engineering, and semantic consistency across tools and applications. How you'll CREATE: * Lead and manage a team of data engineers delivering enterprise-grade data products, data marts, and AI-ready data assets. * Lead the strategic design and delivery of scalable data pipelines and architectures that support commerce analytics, machine learning, and AI workloads. * Partner with Ecommerce, Product, Growth/Marketing, Finance, and Data Science stakeholders to enable AI use cases, feature stores, and model-ready commerce datasets. * Champion engineering best practices for AI-ready data foundations, including data quality, completeness, consistency, lineage, customer identity resolution, and trusted order/product/customer domains. * Lead development and implementation of semantic layers that standardize business definitions. * Ensure enterprise data is structured and documented to support LLMs, knowledge graphs, personalization, forecasting, and downstream AI applications. * Own platform reliability, including SLAs, monitoring, observability, and incident management for ecommerce analytics and AI data pipelines. * Lead the implementation and enforcement of data contracts and schema governance to improve stability and usability for AI and analytics consumers. * Lead adoption of modern data architecture patterns (lakehouse, real-time streaming, batch processing, feature stores, data monitoring, and observability). * Guide platform optimization for performance, scalability, freshness, and cost while supporting high-volume ecommerce data and compute-intensive AI workloads. * Collaborate with governance teams to ensure data is discoverable, explainable, and compliant, especially for AI use cases. * Mentor and develop engineering talent, fostering expertise in data engineering and AI data readiness principles. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)