Senior Manager, Data Engineering

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.

- 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.

- 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.