Overview
Full Time
Skills
Innovation
Sales
Customer Experience
Channel Sales
Real-time
Analytics
Business Intelligence
Reporting
Analytical Skill
FOCUS
Adaptability
Scalability
Collaboration
Cloud Computing
Databricks
Snow Flake Schema
Amazon Web Services
Google Cloud Platform
Google Cloud
Microsoft Azure
Data Governance
Meta-data Management
Continuous Integration
Continuous Delivery
Computer Science
Information Systems
Software Engineering
Data Engineering
Extract
Transform
Load
ELT
Streaming
SQL
Database
Cloud Storage
Data Processing
API
Data Architecture
Data Integration
Data Science
Machine Learning (ML)
Artificial Intelligence
Advanced Analytics
Apache Spark
Apache Flink
Apache Kafka
Big Data
Job Details
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/Google Cloud Platform/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
Preferred Qualifications
Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
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/Google Cloud Platform/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
Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.