Data Architect, EMEIA Sales

Do you want to shape how Apple's EMEIA sales organisation makes critical business decisions?

The Sales Business Process Re\-engineering (BPR) team is building a cutting\-edge, AI\-powered retrieval platform.

This system transforms Apple's vast structured sales data and unstructured knowledge into trustworthy, actionable insights for the people who run the business.

We are looking for a highly skilled Data Architect to help build the foundational data and AI platform that will empower our teams. This is a unique opportunity to create infrastructure that matters from day one.

Description

This is a hands\-on, production\-focused data engineering role at the intersection of software development and process optimisation. You will own features end\-to\-end and operate what you ship.

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.

You will not be building prototypes; you will be maintaining and advancing critical infrastructure that drives real business value.

As a key collaborator, you will work closely with sales teams, data scientists, and other engineers, building trust through clear communication and impactful solutions.

You will build robust data pipelines to support evolving business needs.

A critical part of your role involves working with sales, finance, and operations teams across all levels of the organisation.

You must be comfortable onboarding non\-technical users onto a technical platform by simplifying complexities and avoiding technical jargon.

Preferred Qualifications

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 is a significant advantage.

Experience in a B2B commercial context is preferred.

Minimum Qualifications

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; comfortable working across the data stack from ingestion pipelines to model inference layers.

Experience designing and building data lakes or lakehouses, including schema design for heterogeneous structured and unstructured data sources (documents, PDFs, images, tabular data, APIs).

Solid understanding of modern RAG (Retrieval\-Augmented Generation) architectures, including chunking strategies, embedding models, vector store selection, and retrieval evaluation.

Experience building and deploying AI agents and multi\-agent workflows, with awareness of orchestration patterns, tool use, and failure modes in agentic systems.

Familiarity with Model Context Protocol (MCP) or analogous tool\-serving frameworks for connecting language models to live data sources and internal APIs.

Strong grasp of data quality practices, including lineage tracking, schema validation, deduplication, and handling of multi\-source inconsistencies at enterprise scale.

Exceptional ability to translate ambiguous business questions into well\-scoped data and AI problems, and to communicate findings clearly to non\-technical stakeholders.

Experience with cloud data platforms (AWS, GCP, or Azure), including familiarity with object storage, managed vector databases, and serverless compute patterns.

Familiarity with orchestration tools such as Apache Airflow, Prefect, or equivalent for managing multi\-step data pipelines.

Comfortable working in an environment where requirements evolve; able to build iteratively and know when to prototype versus when to productionise.

This listing is from indeed. View original listing ↗