Head Of Data Architecture

Intec Select Ltd
Chatham
1 week ago
Applications closed

Related Jobs

View all jobs

Head of Data Architecture

Head of Data Architecture

Head of Data Architecture

Head of Data Management, RBS International

Head of Data Management, RBS International

Head of Data Management, RBS International

Head of Data Architecture

Our trusted partner is hiring a Head of Data Architecture to lead the data architectural strategy as our client moves from legacy on-premise data into a cloud-first data approach. Our client is seeking a people manager with recent architecture capabilities to deliver new designs & changes to existing / new business data solutions leveraging tools such as Databricks, Synapse, and ER Studio with experience in Azure or AWS. Our client is offering a basic salary of £100,000 to £120,000 + 40% LTIP bonus + car allowance to be based in Chatham or Wolverhampton on a hybrid basis (some meetings can also be in London).

This is an exciting/challenging opportunity. You will lead the data architecture function, set the architectural direction, and establish the enterprise data catalog during a pivotal period in our client's history.

Role and Responsibilities:

  1. Define and maintain the target data architecture and road map (including the build-out of enterprise data platforms and increased use of cloud technologies).
  2. Work with senior stakeholders across our client to drive adoption of the target data architecture.
  3. Establish data architecture frameworks, standards and patterns that ensure consistent wide storage, consumption, and distribution of data.
  4. Lead the scoping, and initial pre-project design of candidate data projects.
  5. Develop and own key data architecture outputs, including a catalog of authoritative sources, ensuring technical design documentation and appropriate design approval process is followed.
  6. Recruit and lead a small but high-performing team of data architects and data analysts.


Essential Experience:

  1. Recent head of or senior management of a data architecture environment, preferably within Financial Services, is a must.
  2. Strong knowledge of data solutions and an ability to translate this into solutions for the broader business is essential.
  3. Recent exposure to modern data architectures using Azure Databricks, Synapse, ER studio etc., is a must-have.
  4. Domain experience in a regulated environment, insurance, finance, or energy is a must-have.
  5. Strong understanding and experience of cloud data architectures in Azure or AWS is a must-have.


Benefits Package:

£120,000 circa salary / 40% LTIP Bonus / Car Allowance / Excellent Pension / Hybrid working / 30 Days Holiday / Medical Cover / Life Cover#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.