Data Architect, EMEIA Sales

Data Architect, EMEIA Sales

Full-Time 70000 - 90000 £ / year (est.) No working from home possible
Apple

At a Glance

  • Tasks: Shape Apple's EMEIA sales decisions with cutting-edge AI and data engineering.
  • Company: Join Apple, a leader in innovation and inclusivity.
  • Benefits: Competitive salary, inclusive culture, and opportunities for growth.
  • Other info: Collaborative environment with a focus on diversity and accessibility.
  • Why this job: Make a real impact by building critical data infrastructure for AI solutions.
  • Qualifications: Experience in data engineering, Python, SQL, and cloud platforms required.

The predicted salary is between 70000 - 90000 £ per year.

Do you want to shape how Apple's EMEIA sales organization 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 from day one.

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

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.

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.

Equal Opportunity Statement

At Apple, we’re not all the same. And that’s our greatest strength. We draw on the differences in who we are, what we’ve experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law.

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Data Architect, EMEIA Sales employer: Apple

Apple is an exceptional employer that fosters a culture of innovation and inclusivity, making it an ideal place for a Data Architect to thrive. With a commitment to employee growth, you will have access to cutting-edge technology and the opportunity to work collaboratively with diverse teams across EMEIA, ensuring your contributions drive real business value. The supportive environment encourages continuous learning and development, allowing you to shape the future of AI-powered solutions while enjoying comprehensive benefits and a strong focus on accessibility.

Apple

Contact Details:

Apple Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Architect, EMEIA Sales

Tip Number 1

Network like a pro! Reach out to current employees at Apple or in similar roles on LinkedIn. A friendly chat can give you insider info and might just lead to a referral.

Tip Number 2

Prepare for the interview by brushing up on your technical skills. Be ready to discuss your experience with data pipelines, AI agents, and cloud platforms. We want to see how you can bring value from day one!

Tip Number 3

Showcase your problem-solving skills! Think of examples where you've turned complex data challenges into actionable insights. This is key for impressing those non-technical stakeholders.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re serious about joining the team.

We think you need these skills to ace Data Architect, EMEIA Sales

Data Engineering
Applied Machine Learning
Python
SQL
Data Lake Design
Schema Design
Retrieval-Augmented Generation (RAG)

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter for the Data Architect role. Highlight your experience with data engineering, AI, and any relevant projects that showcase your skills. We want to see how you can contribute to our cutting-edge platform!

Showcase Your Technical Skills:Don’t hold back on your technical prowess! Be specific about your proficiency in Python, SQL, and any cloud platforms you've worked with. We love seeing concrete examples of your work, so include links to projects or repositories if possible.

Communicate Clearly:Remember, you’ll be working with non-technical stakeholders too. Use clear, jargon-free language in your application to demonstrate your ability to translate complex concepts into understandable terms. This will show us you’re a great fit for our collaborative environment.

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets the attention it deserves. Plus, you’ll find all the details you need about the role and our team there!

How to prepare for a job interview at Apple

Know Your Data Inside Out

As a Data Architect, you’ll need to demonstrate your hands-on experience with data engineering. Brush up on your Python and SQL skills, and be ready to discuss specific projects where you've built data lakes or pipelines. Prepare to explain how you tackled challenges in those projects, especially around schema design and data quality.

Communicate Clearly with Non-Technical Stakeholders

Since you'll be onboarding non-technical users, practice simplifying complex technical concepts. Think of examples where you've successfully communicated findings to stakeholders without a technical background. This will show your ability to bridge the gap between technical and non-technical teams.

Familiarise Yourself with RAG Architectures

Make sure you understand modern Retrieval-Augmented Generation architectures. Be prepared to discuss chunking strategies, embedding models, and how you would evaluate retrieval effectiveness. This knowledge will be crucial in demonstrating your fit for the role.

Show Your Adaptability

The job requires working in an evolving environment, so be ready to share experiences where you've had to pivot or adapt your approach. Discuss how you decide when to prototype versus when to productionise, as this will highlight your practical understanding of the data engineering lifecycle.