Senior Data Engineering Manager, WW Channel Sales

ext-generation decision support systems - powering predictive coverage, experimentation, LLM based insights and intelligence. You'll collaborate with world-class engineers, data scientists, and business leaders to design AI-native data architectures that turn millions of signals into meaningful, human-centric decisions. ","responsibilities":"Own and evolve the data backbone for the decision intelligence platform with Expert Data Management system - that ingests and curates sales programs data..ensuring scalability, quality, and usability across global channel datasets.

Design and lead data models, pipelines, and APIs that power predictive, prescriptive, and experimental decision tools used by Apple's sales programs decision makers

Partner closely with tech infra and data teams to define data requirements, unify source systems, and accelerate data readiness for new features.

Champion automation, observability, and performance - ensuring the platform delivers reliable, real-time insights with minimal latency.

Enable cross-functional integration of data across merchandising, staffing, and training programs, aligning architecture with Apple's data privacy and security principles.

Mentor and grow a high-performing team of data engineers and analysts; foster a culture of technical excellence, creativity, and collaboration.

Drive innovation in AI-native data architecture - including streaming, feature stores, model-serving infrastructure, and feedback loops.

Collaborate on capability security: ensuring data resilience, model reproducibility, and end-to-end lineage for trusted decisions.

Preferred Qualifications

Experience within a consumer tech, retail, or channel organization.

Exposure to decision intelligence, causal inference, or applied machine learning.

Familiarity with Apple's data privacy and data sharing standards.

Experience working in hybrid data-product environments, supporting both operational and strategic decision layers.

Advanced degrees or certifications in data architecture, AI systems, or cloud platforms are a plus.

Minimum Qualifications

15+ years experience in data engineering or architecture roles, with 5+ years experience managing engineering teams at scale.

Proven experience building large-scale distributed data systems (Spark, Databricks, Kafka, Airflow, Snowflake, etc.) in a production environment.

Deep understanding of data modeling, ETL/ELT frameworks, and ML data pipelines.

Strong fluency in SQL, Python, and cloud data ecosystems (GCP/AWS).

Track record of translating complex data into decision-ready structures that power business outcomes.

Familiarity with AI/ML feature engineering, experiment tracking, and data versioning.

Excellent cross-functional communication skills; proven ability to partner with product, engineering, and business leaders to prioritize and deliver with clarity.

A design-minded approach - valuing simplicity, trust, and user empathy as much as performance.

BS/MS in Computer Science, Data Engineering, or related technical discipline.

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