Data Engineer, Finance & Logistics Analytics

Apple Inc.; Sunnyvale (CA); On-site; USD 149,000 - 249,000; Full time

Benefits offered by this job

Medical & dental

Retirement benefits

Employee stock programs

Tuition reimbursement

Relocation

Bonuses/commissions

Job summary

Apple Inc. in Sunnyvale is seeking an experienced Data Engineer for the Operations Finance data platform, focusing on logistics costing, forecasting, and scalable data models.

You will collaborate with logistics, finance, and engineering to build data pipelines and GenAI-enabled reporting tools to improve transparency and decision-making. This hands-on role emphasizes data quality, automation, and scalable architectures using Dataiku and cloud data warehouses such as Snowflake.

Qualifications

• Minimum 5 years of data engineer experience building end-to-end data pipelines.
• Bachelor’s degree in Computer Science, Information Systems, or equivalent.
• Advanced SQL skills for complex transformations, optimization, and troubleshooting.
• Python proficiency for data pipelines and automation.

Responsibilities

• Understand and document finance processes in the logistics & supply chain space.
• Identify and implement process reengineering and automation opportunities to drive efficiency gains.
• Collaborate with financial analysts, engineers, and operations experts to translate data needs into scalable structures.
• Build scalable, flexible data pipelines optimized for analytics, reporting, and AI/GenAI workloads.
• Implement best practices in data modeling, governance, lineage, quality, and documentation.
• Partner with cross-functional teams to support Agentic & GenAI applications.

Skills

SQL

Python

Data pipelines

Analytics

Education

Bachelor's degree in Computer Science or related

Tools

Airflow

Snowflake

PostgreSQL

Job description

Apple Inc. in Sunnyvale is seeking an experienced Data Engineer for the Operations Finance data platform, focusing on logistics costing, forecasting, and scalable data models.

You will collaborate with logistics, finance, and engineering to build data pipelines and GenAI-enabled reporting tools to improve transparency and decision-making. This hands-on role emphasizes data quality, automation, and scalable architectures using Dataiku and cloud data warehouses such as Snowflake.