Data Solutions Engineer, Retail Customer Insights
Apple
Customer Service
Cupertino, CA, USA
USD 146,300-291,400 / year + Equity
Posted on May 20, 2026
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. Apple’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. Apple 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.
- Architect data solutions that support the full research lifecycle, including survey infrastructure, data pipelines, automated reporting systems, and AI-enabled analysis workflows
- Lead the modernization of research operations by identifying opportunities to automate manual processes, improve data quality, and accelerate time-to-insight across the team
- Partner closely with researchers, data analysts, and technical teams to translate business and research needs into robust, scalable technical solutions
- Deploy machine learning and NLP models to extract themes, patterns, and actionable insights from large volumes of structured and unstructured customer feedback
- Maintain the team's tooling ecosystem, ensuring systems are reliable, well-documented, and positioned to scale with growing data volumes and business complexity
- Collaborate with other technical teams across Apple to share best practices, build on existing infrastructure, and encourage adoption of new tools and features
- Communicate technical concepts and designs clearly to both technical and non-technical partners, supporting alignment and enabling self-serve access to data and insights across the organization
- 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
- Proficiency in Python or R, and experience with SQL and data querying at scale (e.g., Snowflake, BigQuery)
- Hands-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
- Proficiency with data visualization and reporting platforms such as Tableau, Streamlit, and similar tools
- Exposure to survey platforms and systems (e.g., Qualtrics, Medallia) and an understanding of how survey data flows from collection through analysis
- Expertise applying large language models (LLMs) and generative AI, including practical experience integrating these technologies into data products or analytical workflows
- Familiarity with survey methodology, research concepts, and customer experience measurement (e.g., NPS, CSAT)
- Strong documentation skills, with experience creating system diagrams, data flow documentation, and technical specifications (e.g., using Miro, Lucidchart, or similar)
- Self-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
- Ability to tailor your communications for a variety of audience types, from technical partners to senior executives