What are the responsibilities and job description for the Data Solutions Engineer, Retail Customer Insights position at Apple, Inc.?
Imagine what you could do here! The people here at Apple don't just create products - they build 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. At Apple, inclusion is a shared responsibility, and we work together to foster a culture where everyone belongs and is inspired to do their best work.\\n\\nApple's worldwide Retail Engagement, Marketing and Merchandising (REMM) team creates and delivers programs, campaigns, initiatives, and experiences that help Apple Retail's customers and teams discover, buy, and go further with Apple products and services. These efforts increase awareness, drive conversion, and grow affinity for Apple.\\n\\nApple Retail's Customer Insights team is looking for a Data Solutions Engineer to help us build the tools and systems that transform how we listen to our customers and teams. In this role, you will bring together data science, research operations, and emerging technologies to directly impact decisions across all of Apple's global direct channels (Apple Stores, Apple.com, the Apple Store app, and other digital platforms). As our lead architect, you will tackle complex data challenges and build the infrastructure that amplifies the reach and impact of our research.
The Data Solutions Engineer will design the data infrastructure, pipelines, and machine learning workflows that power Apple Retail's research programs. By partnering closely with our team of researchers and program managers, you will help modernize how we collect, process, and share findings. Whether you are enhancing our survey systems or integrating new LLM capabilities into our analysis, your work will directly improve the speed and depth of the insights shaping Retail's strategic direction.
8 years of experience in data engineering, data science, or a related technical field, with demonstrated ability to build and ship production-quality data solutions\nProficiency in Python or R, and experience with SQL and data querying at scale (e.g., Snowflake, BigQuery)\nHands-on experience building or applying machine learning and AI techniques to real-world data problems (e.g., NLP, LLMs, classification, clustering)
Experience modernizing or operationalizing research or analytics workflows, including building automation pipelines and integrating AI-powered tools into existing processes\nProficiency with data visualization and reporting platforms such as Tableau, Streamlit, and similar tools\nExposure to survey platforms and systems (e.g., Qualtrics, Medallia) and an understanding of how survey data flows from collection through analysis\nExpertise applying large language models (LLMs) and generative AI, including practical experience integrating these technologies into data products or analytical workflows\nFamiliarity with survey methodology, research concepts, and customer experience measurement (e.g., NPS, CSAT)\nStrong documentation skills, with experience creating system diagrams, data flow documentation, and technical specifications (e.g., using Miro, Lucidchart, or similar)\nSelf-directed and comfortable navigating ambiguity in a fast-paced, highly matrixed environment, with the ability to manage competing priorities and adapt quickly to changing business needs\nAbility to tailor your communications for a variety of audience types, from technical partners to senior executives
The Data Solutions Engineer will design the data infrastructure, pipelines, and machine learning workflows that power Apple Retail's research programs. By partnering closely with our team of researchers and program managers, you will help modernize how we collect, process, and share findings. Whether you are enhancing our survey systems or integrating new LLM capabilities into our analysis, your work will directly improve the speed and depth of the insights shaping Retail's strategic direction.
8 years of experience in data engineering, data science, or a related technical field, with demonstrated ability to build and ship production-quality data solutions\nProficiency in Python or R, and experience with SQL and data querying at scale (e.g., Snowflake, BigQuery)\nHands-on experience building or applying machine learning and AI techniques to real-world data problems (e.g., NLP, LLMs, classification, clustering)
Experience modernizing or operationalizing research or analytics workflows, including building automation pipelines and integrating AI-powered tools into existing processes\nProficiency with data visualization and reporting platforms such as Tableau, Streamlit, and similar tools\nExposure to survey platforms and systems (e.g., Qualtrics, Medallia) and an understanding of how survey data flows from collection through analysis\nExpertise applying large language models (LLMs) and generative AI, including practical experience integrating these technologies into data products or analytical workflows\nFamiliarity with survey methodology, research concepts, and customer experience measurement (e.g., NPS, CSAT)\nStrong documentation skills, with experience creating system diagrams, data flow documentation, and technical specifications (e.g., using Miro, Lucidchart, or similar)\nSelf-directed and comfortable navigating ambiguity in a fast-paced, highly matrixed environment, with the ability to manage competing priorities and adapt quickly to changing business needs\nAbility to tailor your communications for a variety of audience types, from technical partners to senior executives