Director Data Architect

Xpand Group
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Digital Data Consultant, Data Engineering, Data Bricks, Part Remote

Digital Data Consultant, Data Engineering, Data Bricks, Part Remote

Head of Data Strategy

Data Analytics Manager - Heartwood Collection

Associate Director - Data Analytics

Data Architect - Permanent Role | Switzerland | Excellent Salary & Benefits
Shape the Future of Enterprise Data Architecture

This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board.


Are you passionate about building world‑class data solutions that power business transformation?


We are seeking an experienced Data Architect to join a leading AI & Data team in Switzerland. This is a permanent position offering exceptional salary, benefits, and career growth opportunities - with the chance to design and lead innovative, enterprise‑scale solutions for top‑tier clients across industries.


As a Data Architect, you'll play a strategic and hands‑on role, guiding organizations on their data modernization journey. You'll work with senior stakeholders to shape enterprise data ecosystems, set architectural standards, and ensure the successful implementation of cutting‑edge cloud and AI‑driven data platforms.


Your impact areas will include:



  • Enterprise Data Architecture Design
  • Modernize complex, multi‑system landscapes (MDM, CRM, ERP, Cloud DWH) through pragmatic and scalable architectural blueprints.
  • Cloud Data Platform Leadership
  • Design and implement high‑performance cloud data platforms (AWS, Azure, Google Cloud, Databricks, Snowflake), overseeing data modelling, integration, transformation, and DevOps pipelines.
  • Integrated Solution Architecture
  • Design seamless integrations between cloud data platforms, AI/GenAI platforms, and business‑critical systems (e.g., MDM, CRM).
  • Market Thought Leadership
  • Represent the architecture capability at events, conferences, and client discussions - strengthening the firm's market presence in Switzerland.
  • Mentorship & Capability Building
  • Coach junior team members, contribute to internal expertise development, and collaborate with nearshore/offshore teams to drive innovation and excellence.

What We're Looking For
Experience & Skills

  • 4+ years as a Data Architect, leading design and implementation of complex cloud‑based data ecosystems.
  • Solid engineering background with hands‑on data platform implementation experience (AWS, Azure, GCP, Databricks, or Snowflake).
  • Proven ability to evaluate data architecture decisions, influence business and IT stakeholders, and define strategic data direction.
  • Strong understanding of coding best practices, code quality tools (e.g., SonarQube), and modern AI‑assisted development tools.
  • Deep experience with multiple database models - relational, NoSQL, and graph-based (knowledge graph).
  • Nice to have: experience using Infrastructure as Code (IaC) tools such as Terraform.

Why Join?

  • Competitive Swiss market salary with comprehensive benefits package
  • Work on strategic, large‑scale projects with major global clients
  • Continuous training and certification opportunities
  • Hybrid working model and flexibility
  • A collaborative, inclusive, and innovation‑driven culture

Location

Switzerland - with flexible working options.


*Rates depend on experience and client requirements


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.