Data Architect – Multi-Cloud – Eligible for Security Clearance

London
13 hours ago
Create job alert

Data Architect – Multi-Cloud / Data Platforms – Eligible for Security Clearance

I’m working with a leading Tech Consultancy that have secured a number of long term client projects and are looking for a number of Data Architects to support increased project demand.

It’s a good opportunity if you’re looking to move away from being tied to one system or stack and instead get involved in designing end-to-end data platforms across different environments and industries with the ability to steer your own career.

What you’ll be doing

The role:

  • This is an end-to-end architecture role, so you won’t just be focused on one part of the data lifecycle.

  • Designing platforms from data ingestion through to consumption, making sure everything flows properly end-to-end

  • Working with modern approaches such as lakehouse and medallion architectures (Bronze, Silver, Gold)

  • Building out both batch and streaming pipelines, depending on the use case

  • Making decisions around storage, orchestration, governance and security, rather than just implementing someone else’s design

    A big part of the role is also working closely with clients, so you’ll need to be comfortable explaining your thinking and translating business requirements into practical solutions, working with technical and non-technical audiences.

    Security Clearance

    DV or SC Clearance would be preferred but they are happy to those who are eligible to achieve clearance in the near future (i.e. UK Citizen and lived her for over 5 years, no periods of more than 30 days outside the country in the last 5 years and no more than 6 months out the country in a calendar year).

    Tech environment

    The environment is multi-cloud, so you’re not restricted to one platform and will be working across different client setups.

    Typically, that includes:

  • AWS, Azure and GCP depending on the project

  • Tools like Databricks, Snowflake and Synapse

  • Spark and distributed processing frameworks for large-scale data

  • Streaming technologies such as Kafka or Kinesis

    You will use your expertise to choose the right tools for the problem, rather than just defaulting to a single stack.

    What they’re looking for

    They’re looking for people who have experience designing full data platforms and can think beyond just individual components.

    That usually means:

  • A strong background in data engineering or architecture, with hands-on experience

  • A good understanding of modern data patterns like lakehouse, medallion, and streaming

  • Someone comfortable working with stakeholders or clients, not just internally

  • A practical mindset, where you can make sensible trade-offs rather than over-engineering

    Why it’s worth considering

    What tends to appeal to people is the variety and level of ownership in the role. You’ll be working on different projects across different industries with the ability to steer your career based on your skills and interests. There’s a good balance between technical work and client interaction an you’ll get exposure to a range of tools, platforms, and approaches

    Salary: £100,000 + bonus + good pension

    Location: London – Hybrid working 2 days a week in the office or London based client sites.

    APPLY NOW FOR IMMEDIATE CONSIDERATION

Related Jobs

View all jobs

Lead Data Architect - Multi-Cloud & Real-Time Pipelines

Senior and Principle Data Architect (multiple roles)

Senior Data Architect – Hybrid, Public Sector Impact

Data Architect (DV)

Data Architect — Hybrid, Multi-Cloud AI Platforms

Data Architect

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.