Data Architect

Indotronix Avani UK Ltd
Bristol
2 weeks ago
Applications closed

Related Jobs

View all jobs

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Data Architect

Title: Cloud Data Architect/Data Architect


Location: Bristol & London- UK - Hybrid- 2 Days a Week Onsite (Client preference is Bristol, but they will consider London as well)


Pay Rate/Salary: (Depends on Experience level) £55,000-£75,000 per annum


Type: Permanent/Full time job


Required: Eligible for UK Security Clearance (or 5+ years UK residency).


Responsibilities

  • Architect cloud-based data platforms and pipelines to enable Data Engineering, Data Science and Data Exploitation teams to develop and build solutions that deliver client requirements, integrating into the existing client infrastructure (Azure, AWS, OCI, GCP, MOD Cloud or hybrid).
  • Lead the design and development of cloud-based data systems such as data warehouses, data lakes, streaming platforms and analytics pipelines using appropriate cloud services and infrastructure.
  • Lead client engagement activities to elicit technical requirements, enable solution design, architecture and infrastructure definition, and lead delivery of cross‑skilled teams.
  • Oversee internal and client project data architecture and data governance.
  • Translate customer business needs and ambiguous problem statements into clear technical designs.
  • Ensure solutions meet required Government security, governance and regulatory requirements.
  • Lead customer workshops, solution design activities and stakeholder engagement.
  • Lead code reviews, providing best‑practice guidance and ensuring adherence to regulatory requirements.
  • Provide architectural oversight throughout project delivery life cycles.
  • Mentor and develop junior and mid‑level team members.
  • Contribute to thought leadership and the development of the technical delivery community.
  • Collaborate within Techmodal and across Digital Intelligence and Client Systems to ensure alignment and growth of the Data Solutions community.
  • Work with the Business Development team to provide technical input into bids, including machine learning, large language models and data analytic solutions.

Qualifications

  • Significant domain experience building and delivering software and data capabilities, with a track record of shaping delivery offerings in a secure data environment.
  • Experience architecting cloud data platforms in Defence, Government, Healthcare, Nuclear or other highly regulated sectors.
  • Hands‑on expertise implementing analytics, data science and software across various cloud and on‑premises ecosystems.
  • Ability to adapt and develop new approaches when projects face setbacks, and apply lessons learned to future design and architecture solutions.
  • Strong understanding of UK Government secure data design principles, including Secure by Design.
  • Awareness of relevant security & regulatory frameworks (NCSC, ISO 27001, NIST, GDPR).
  • Ability to explain complex concepts to non‑technical audiences.
  • Ability to influence diverse stakeholders, including senior leadership.

  • Collaborative and inclusive mindset.
  • Experience developing people and sharing knowledge, creating training and frameworks to facilitate team development and upskilling.


#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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.