The Finance Business Process Reengineering (BPR) organization supports every AppleFinance function worldwide. Our Finance Data team within the BPR org enables the Finance organization by providing quality data accessibility, analytics, reporting and automation services. We are looking to expand capabilities in the areas of data privacy, high-performance computing, advanced analytics and general business intelligence. A Finance Data Engineer is a technical expert and works tightly with other data engineers and data analysts on a team to create data integrations, ETL, pipelines and codebase to drive innovative analytics projects from initial experimentation to production level deployment. They work on critical data engineering problems, building bespoke, reliable, accurate, consistent, and architecturally sound solutions that are aligned with business needs. The Finance Data Engineer architects, maintains and continually improves data analytics and automation capabilities. The role requires working cross-functionally with business users, Apple IS&T developers, and data analysts to deliver complete, accurate, well-secured data that enables reporting and analytics. This role is required to learn and perform a wide variety of development and engineering tasks, on top of growing their knowledge of Finance business processes to efficiently identify data applicable business questions. Finance Data Engineers work predominately in Apple’s enterprise data warehouse (EDW), identifying and combining data in an efficient, scalable manner to help answer business questions. When the necessary data is not available on an enterprise system or is generated offline by business users or third parties, the Finance Data Engineer must develop methods to reliably source, validate, and integrate the data into EDW. The Finance Data Engineer must learn and understand a variety of available IS&T solutions, when and how to use them, and when to develop custom solutions. This paired with a proven record of excellent problem solving and a sharp, open mind will be more important than deep expertise in any one area.
Education & Experience
Undergraduate degree in Computer Science, MIS, Engineering, Mathematics or other quantitative field required.View More