Product Marketing Customer Analytics Engineer

Imagine what you could do here. At Apple new ideas have a way of becoming great products services and customer experiences very quickly. Bring passion and dedication to your job and theres no telling what you could accomplish. The Product Marketing Customer Analytics team is seeking a engineer to support customer analytics with advanced scalable and robust architecture tools data products and critical data pipelines that are optimized for rapid business intelligence data analysis and data science.n

Develop and automate large scale high-performance scalable platform (batch and/or streaming) to drive faster analyticsnAbility to design large-scale complex applications and frameworks with excellent run-time characteristics such as low-latency fault-tolerance and availabilitynExperience in building and maintaining custom frameworks to support engineering/analytics needsnKnowledge of continuous integration testing methodologies TDD and agile development with analytic consumers and data scientists to build and improve new/existing constructs and solve data engineering problems at in building data pipelines in Spark Trino lakehouse or similar distributed platforms u0026 inclusive data quality checks to ensure high quality of high quality software engineering practices towards building data infrastructure and pipelines at scale. nStructured thinking with ability to easily break down ambiguous problems and propose impactful solutions. nApplying Generative AI and Retrieval Augmented Generation (RAG) techniques to enhance data analytics capabilitiesnCommunication Strong documentation and technical writing to detail and effective verbal/written communication skills.n

3 years of relevant Engineering experiencenUndergraduate degree in Computer Science MIS Engineering Mathematics or other quantitative discipline required.n

3 years of experience in data engineering and ETL pipeline developmentn2 years of experience in Big Data Technologies (SparkLakehouseTrino)nExperience on Kubernetes Docker with Retrieval Augmented Generation (RAG) techniques working in conjunction with LLMsnExperience with creating and consuming Model Context Protocol (MCP) servicesnSnowflake knowledge