Senior Data Engineer, WW Channel Strategy & Operations
Austin, Texas, United States Sales and Business Development Summary Posted:
Mar 29, 2025 Weekly Hours:
40 Role Number:
Imagine what you could do here. The people here at Apple don't just create products - they create the kind of wonder that's revolutionized entire industries. It's the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. Apple's WW Channel Strategy & Operations (CSO) organization focuses on developing and deploying worldwide sales programs and standard processes to deliver an extraordinary customer experience in the channel and drive Apple Channel sales. With deep functional expertise in digital, physical, and people enablement spaces, our WW CSO team closely collaborates with many cross-functional groups at world-wide and regional levels. We are looking for an experienced data engineer to help to transform large, near-real-time data into valuable, actionable datasets. You also would be helping to build and scale the data processing platform that will fuel analytics and insights for the CSO organization.
Description As part of the role, you would work closely with data scientists, BI and reporting analysts, and business and product teams to build scalable data pipelines and solutions. Effective collaboration across other data engineering teams and business teams will be critical for creating scalable, sustainable analytical solutions. In this role, you will: - Develop and drive customer-focused solutions based on developing a deep understanding of user requirements - Translate user needs into actionable solutions and act as a subject matter expert in customer discussions - Scope and prioritize system development with a focus on incremental delivery and an iterative approach, ensuring adaptability to evolving project requirements - Design and implement modern, distributed data architectures aligned with data mesh principles (domain ownership, product thinking, self-serve platforms, and federated governance) - Lead the development and deployment of data products across business domains, ensuring scalability, interoperability, and reliability - Collaborate with domain teams to define and build domain-aligned data models, pipelines, and APIs - Architect cloud-native data platforms using tools and services such as Databricks, Snowflake, AWS/GCP/Azure, Kafka, Delta Lake, etc. - Define and enforce data governance, metadata management, quality, lineage, and observability standards - Partner with cross-functional teams (Data Scientists, Product Managers, Business Units) to translate business needs into technical solutions - Evaluate, recommend, and integrate new tools, frameworks, and practices to improve the data architecture and engineering ecosystem - Drive automation, CI/CD practices, and Infrastructure-as-Code (IaC) for data solutions
Minimum Qualifications - Bachelor's or Master's degree in Computer Science, Information Systems, Software Engineering, Data Science, or a related field
- 12+ years industry experience in data engineering or related technical field
- 5+ years experience in building and maintaining large-scale ETL/ELT pipelines (batching and/or streaming) that are optimized for performance and can handle data from various sources, structured or unstructured
- Experience developing automation to write and read data from relational, no-SQL databases, from cloud storage and external data sources
Preferred Qualifications - Hands-on experience in the realm of large-scale data processing infrastructure, including building and maintaining API-driven services
- Strong hands-on programming skills with a track record of developing robust, scalable, and maintainable codebases for intricate data infrastructure
- Ability to lead technical discussions about data architecture and data integration
- Familiarity with other related fields, such as data science, machine learning, and artificial intelligence, to design solutions that can accommodate advanced analytics
- Familiarity with a diverse set of technologies, including but not limited to Spark, Flink, Trino, Kafka, Iceberg, in the big-data ecosystem
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