Data Architect, Emeia Sales

Apple Apple · Big Tech · London, United Kingdom +1 · Sales and Business Development

Apple is seeking a Data Architect to build a foundational data and AI platform for its EMEIA sales organization. This role focuses on creating a cutting-edge, AI-powered retrieval platform that transforms sales data into actionable insights, serving as the backbone for AI assistants and copilots. It's a hands-on, production-focused data engineering role involving end-to-end feature ownership and operational responsibility.

What you'd actually do

  1. This is a hands-on, production-focused data engineering role at the intersection of software development and process optimisation.
  2. You will own features end-to-end and operate what you ship.
  3. The platform you build will serve as the backbone for the AI assistants, copilots, and natural-language interfaces that EMEIA Sales will rely on over the next several years.
  4. You will build robust data pipelines to support evolving business needs.
  5. A critical part of your role involves working with sales, finance, and operations teams across all levels of the organisation.

Skills

Required

  • Data engineering
  • Applied machine learning
  • Python
  • SQL
  • Data lakes or lakehouses
  • Schema design
  • RAG architectures
  • Embedding models
  • Vector store selection
  • Retrieval evaluation
  • AI agents
  • Multi-agent workflows
  • Orchestration patterns
  • Tool use
  • Model Context Protocol (MCP)
  • Tool-serving frameworks
  • Data quality practices
  • Lineage tracking
  • Schema validation
  • Deduplication
  • Cloud data platforms (AWS, GCP, or Azure)
  • Object storage
  • Managed vector databases
  • Serverless compute patterns
  • Orchestration tools (Apache Airflow, Prefect)

Nice to have

  • Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Applied Mathematics, or a related technical field, or equivalent practical experience.
  • Experience working with partner, reseller, or retail channel data
  • Experience in a B2B commercial context

What the JD emphasized

  • production-focused
  • own features end-to-end and operate what you ship
  • critical infrastructure
  • Proven hands-on experience in data engineering, applied machine learning, or a closely related field, with demonstrable work shipped to production.
  • Strong proficiency in Python and SQL
  • Solid understanding of modern RAG (Retrieval-Augmented Generation) architectures
  • Experience building and deploying AI agents and multi-agent workflows
  • Familiarity with Model Context Protocol (MCP) or analogous tool-serving frameworks
  • Strong grasp of data quality practices
  • Exceptional ability to translate ambiguous business questions into well-scoped data and AI problems
  • Experience with cloud data platforms (AWS, GCP, or Azure)

Other signals

  • AI-powered retrieval platform
  • AI assistants, copilots, and natural-language interfaces
  • foundational data and AI platform