Data Engineer, PART DataOps

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.\n\nWe 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.

6+ years software engineering, including strong SQL and data focus\nBS in Engineering / Computer Science\n

2+ years leading or mentoring other engineers\nExpertise in languages like Python and technologies like Airflow, Spark, Trino, Kafka, Docker, Iceberg, Databricks\nHands-on experience with database design and architecture in cloud data warehouses and lakehouse environments\nAbility to analyze complex datasets and design solutions with optimum quality and efficiency\nAppreciation for data quality and validation in every pipeline\nFamiliarity with SDLC best practices, version control, CI/CD\nStrong communication skills with the ability to translate between technical and business contexts\nExperience with cloud services such as AWS, Google Cloud Platform, or Azure for data infrastructure and storage\nExperience with streaming interfaces and pipelines\nFinance and accounting process experience

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.\n\nApple 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.