Senior Data Engineer & Scientist — Apple Korea

Apple

14h

About The Position

At Apple, extraordinary products begin with a deep understanding of the people who use them. Our Korea Channel Sales team leverages data to shape business strategy, optimize customer experiences, and drive growth across channels and partners. As a Senior Data Engineer & Scientist, you will build the data infrastructure that connects fragmented data sources across the business — and apply that foundation to generate insights, run experiments, and develop AI-driven solutions. This dual role is ideal for someone who can architect reliable data systems and, with that same depth, design the experiments and models that turn data into business decisions. In this role, you will own the full data lifecycle — from pipeline architecture and data modeling through to statistical analysis and AI-driven insight generation. You will work closely with cross-functional teams spanning marketing, business operations, partner management, and engineering. Early on, you will focus on standing up data infrastructure: building ingestion pipelines, automating dashboards, identifying data gaps, and ensuring data governance and compliance. As the foundation matures, you will shift toward data science — designing experiments, generating customer insights, and applying machine learning to optimize initiatives for sales growth. You will also contribute to the development of data platforms that enable seamless collaboration with external and internal partners, and help scale analytics capabilities across the organization.

Requirements

5+ years of relevant work experience in data engineering, data science, or a combined role, with hands-on depth across both disciplines
Proficiency in Python and SQL, with experience building and maintaining ETL/ELT pipelines, data models, and automated dashboards
Experience with marketing data science methods including experimental and testing design, regression analysis, and causal inference
Solid understanding of data governance, compliance, and access control principles
Strong problem-structuring skills, with the ability to work through ambiguity and define clear analytical approaches
Excellent communication and stakeholder management skills, with the ability to present to non-technical business audiences
Fluent verbal and written communication skills in both Korean and English

Nice To Haves

Experience with cloud data platforms (e.g., Snowflake)
Proficiency in machine learning methods applied to customer analytics, business optimization, or forecasting
Experience building data-sharing platforms or partner data integration workflows
Background in business analytics (e.g., attribution, funnel analysis, customer lifetime value, marketing mix modeling)
Exposure to digital advertising, marketplaces, e-commerce, media, or consumer technology business related analysis
Experience identifying repeatable business problems and converting them into scalable tools or automated workflows
Master’s or PhD in Computer Science, Statistics, Data Science, Economics, Operations Research, Engineering, or a related quantitative field with 5+ years of relevant experience

Responsibilities

Design, build, and maintain scalable data pipelines and automated reporting infrastructure to support business priorities across the Korea Channel Sales organization
Identify critical data gaps and drive the acquisition and integration of new data sources from internal systems and external partners
Establish data governance, compliance, and access control frameworks to ensure data quality and reliability
Design and execute tests and experiments to measure business impact and optimize customer engagement strategies
Apply machine learning, segmentation, and statistical methods to generate actionable insights and growth recommendations
Build and operate data platforms that enable structured data collaboration with external partners
Translate business requirements into analytical frameworks, reusable data products, and automated workflows
Leverage AI and LLM-based tooling to streamline analysis, automate tasks, and accelerate insight generation
Communicate complex analytical and technical findings clearly to both technical and non-technical stakeholders
Partner with worldwide counterparts to align data capabilities and analytical standards globally