Finance Data Engineer
Confirmed on the employer's own hiring board on Oct 22, 2026. First seen by Alion on Jun 9, 2026. Apple scores A on the Alion truth index.
The Finance Data Engineer is a technical expert who creates data interfaces, pipelines and codebase that drives innovative data products for Apple Finance. They build reliable, accurate, consistent, and architecturally sound solutions that are aligned with business needs.
This role requires working cross-functionally with business users, IS&T, data scientists and other engineers to develop and deploy data services and pipelines. An ability to acquire knowledge of Finance business processes is important.
You will be working in an enterprise data warehouse and lakehouse environments to help identify and combine data in an efficient, scalable manner to help answer business questions.
Description
- Work closely with data scientists, machine learning engineers, software engineers, and business partners to identify, capture, collect, load and format data from the external sources, internal systems and the data warehouse.
- Develop, test, deploy, monitor, document and troubleshoot data pipelines and feature-ready datasets
- Collaborate with other engineers to define and adopt best practices for translate finance use cases into data requirements, schemas, and retrieval patterns for RAG, agents, and other LLM workflows
- Identify and review capabilities of emerging technologies and to enable the adoption of these new technologies and associated techniques
Minimum Qualifications
5+ years of relevant Data Engineering experience
Undergraduate degree in Computer Science, MIS, Engineering, Mathematics or other quantitative discipline required with five or more years of experience
Preferred Qualifications
Effective Python, shell and SQL programmer
Hands on experience with database design and architecture in cloud data warehouses (Snowflake) and lakehouse environments (s3)
Ability to implement end to end encryption and decryption policies as part of sensitive data pipelines and semantic views or other data sources
Experience with the data development lifecycle and its associated CI/CD and version control components and tooling (Jenkins, Git, Other)
Exposure to cloud storage and orchestration tooling such as AWS and Kubernetes
Experience with streaming interfaces and pipelines a plus
Ability to implement data and automation services via RESTful interfaces
Appreciation for data quality and validation in every pipeline
Finance and accounting process experience a plus