Technical Architect

La Fosse
Bristol
1 year ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Senior Data Scientist - Hertfordshire, HP2 4YL

Data Governance Lead

Technical Data Architect

Technical Architect (AWS) - 90% Remote working - £600 per day (Outside IR35) - 6 Month contract


I am seeking a highly skilled Technical Architect with expertise in AWS and a strong focus on architecture to lead the transformation of our data platform.


Key Responsibilities:

  • Lead the architectural design and implementation of a robust AWS-based data platform.
  • Work closely with stakeholders to define and execute the data strategy, ensuring scalability and long-term viability.
  • Collaborate with the incumbent Enterprise Architect and bridge the gap between high-level strategy and hands-on technical delivery.
  • Address complex data challenges and streamline data workflows across the organization.
  • Support the rollout of a smaller ERP (Nursery Management System) while ensuring data consistency and integrity.
  • Provide leadership and technical guidance to project teams, while remaining hands-on in solution architecture.


Key Requirements:

  • Proven experience as a Technical Architect with a strong background in AWS.
  • Expertise in data architecture, including the ability to assess and resolve complex data-related challenges.
  • Hands-on technical capability to work at a detailed level while maintaining a strategic perspective.
  • Experience working on large-scale data transformation projects.
  • Strong problem-solving skills with the ability to simplify complex data structures.
  • Excellent communication and leadership skills.

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.