Data Architect

Syntax Consultancy Ltd
Leeds
2 months ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

GCP Data Architect

Data Architect – Mainframe Migration & Modernization

Data Architect
Leeds (Hybrid)
Permanent
£85,000 (DOE)

Data Architect needed for a permanent career opportunity based in Leeds (Hybrid). Start ASAP ideally Jan 2026.


Hybrid Working - 2 days/week remote (WFH), and 3 days/week working on-site in the Leeds office.


A chance to work with a leading global IT transformation business specialising in large-scale Government projects.



  • Data Architect with in-depth experience of data modelling for both NoSQL and relational databases, including structured and unstructured data environments.
  • Defining / designing data models, data flows + data lifecycle processes across multiple integrated systems.
  • Strong experience of AWS cloud platforms, NoSQL technologies (-eg- DynamoDB), data lineage, metadata + traceability tools (-eg- Solidatus).
  • Key Tasks include: defining logical/physical data models, NoSQL data modelling, NoSQL data structures, data flows, data requirements, data lineage, data integration + data governance standards.
  • Strong understanding of system integrations, including API-based workflows, data exchange patterns + documentation of data integration specifications.
  • Working collaboratively with technical teams, data governance groups, and senior stakeholders to ensure that data structures, documentation and processes support business goals.
  • Certifications in data architecture, cloud platforms, or data governance are advantageous.
  • Must either hold active SC security clearance, or be fully eligible to undergo the SC Security Clearance vetting process.
  • Benefits: Salary to £85k (DOE) + Hybrid Working + Bonus + Pension + 22 days holiday plus BHs (rising to 25 days) + Death in Service + Health Care Plan + More.


#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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.