Data Engineer, PART DataOps

AppleCupertino, CA
7h

About The Position

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. Apple is seeking an experienced, detail-minded data engineering team lead to join our Finance Process, Analytics, Reporting & Technology (PART) Data Operations team. You will lead a team of data engineers delivering trustworthy, high-quality data to our Retail Finance analysts. If you are someone who looks forward to solving complex business problems, developing engineering talent, and building reliable data foundations, please reach out to us. You will lead a team of data engineers to architect, develop, and test large scale and efficient solutions that provide Apple leadership with the accurate data required to rapidly understand and adapt to changing business conditions. We are a growing team with plenty of interesting technical and business challenges to solve. We seek a self-starter who is willing to learn fast, adapt well to changing requirements, develop others, and work with cross functional teams.

Requirements

  • 6+ years software engineering, including strong SQL and data focus
  • BS in Engineering / Computer Science

Nice To Haves

  • 2+ years leading or mentoring other engineers
  • Expertise in languages like Python and technologies like Airflow, Spark, Trino, Kafka, Docker, Iceberg, Databricks
  • Hands-on experience with database design and architecture in cloud data warehouses and lakehouse environments
  • Ability to analyze complex datasets and design solutions with optimum quality and efficiency
  • Appreciation for data quality and validation in every pipeline
  • Familiarity with SDLC best practices, version control, CI/CD
  • Strong communication skills with the ability to translate between technical and business contexts
  • Experience with cloud services such as AWS, GCP, or Azure for data infrastructure and storage
  • Experience with streaming interfaces and pipelines
  • Finance and accounting process experience
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