At a Glance
- Tasks: Shape Apple's EMEIA sales decisions with an AI-powered data platform.
- Company: Join Apple, a leader in innovation and inclusivity.
- Benefits: Competitive salary, inclusive culture, and opportunities for growth.
- Other info: Collaborative environment with diverse teams and evolving challenges.
- Why this job: Make a real impact by building critical infrastructure from day one.
- Qualifications: Experience in data engineering and proficiency in Python and SQL required.
The predicted salary is between 60000 - 80000 £ per year.
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.
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.
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 Employment 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: Omaze
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 the opportunity to work on cutting-edge AI technologies while collaborating with diverse teams across the EMEIA region. The company offers competitive benefits and a supportive environment that values your contributions from day one, ensuring that you can make a meaningful impact in shaping the future of sales processes.
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 even lead to a referral, which is always a bonus!
✨Tip Number 2
Show off your skills! Prepare a portfolio or a GitHub repository showcasing your data engineering projects. This way, you can demonstrate your hands-on experience and problem-solving abilities during interviews.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions related to data architecture and AI. Mock interviews with friends or using online platforms can help you articulate your thoughts clearly.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in being part of the Apple team. Don’t miss out on this opportunity!
We think you need these skills to ace Data Architect, EMEIA Sales
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, Python, and SQL, and show how your skills align with the job description. We want to see how you can contribute to our cutting-edge AI-powered platform!
Showcase Your Projects:Don’t just list your skills; demonstrate them! Include specific examples of projects you've worked on that relate to data lakes, AI agents, or cloud platforms. This will help us understand your hands-on experience and how you’ve shipped real solutions in the past.
Communicate Clearly:Remember, we value clear communication! When describing your experience, avoid jargon and explain complex concepts in simple terms. This is especially important since you'll be working with non-technical stakeholders, so show us you can bridge that gap.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our culture and values!
How to prepare for a job interview at Omaze
✨Know Your Data Inside Out
Make sure you’re well-versed in the data engineering concepts mentioned in the job description. Brush up on your Python and SQL skills, and be ready to discuss your experience with data lakes and RAG architectures. Being able to talk confidently about your past projects will show that you can hit the ground running.
✨Simplify the Complex
Since you'll be onboarding non-technical users, practice explaining technical concepts in simple terms. Prepare examples of how you've successfully communicated complex ideas to stakeholders in the past. This will demonstrate your ability to bridge the gap between technical and non-technical teams.
✨Showcase Your Problem-Solving Skills
Be ready to discuss how you've tackled ambiguous business questions in previous roles. Think of specific examples where you translated vague requirements into actionable data solutions. This will highlight your analytical thinking and adaptability, which are crucial for this role.
✨Familiarise Yourself with Cloud Platforms
Since experience with cloud data platforms is essential, make sure you understand the basics of AWS, GCP, or Azure. Be prepared to discuss any hands-on experience you have with these platforms, especially regarding object storage and serverless compute patterns. This knowledge will set you apart from other candidates.