Data Architect- Senior Manager

PwC UK
Leeds
4 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 - Senior Manager


PwC UK


About the Role

We are seeking a highly experienced and strategic Data Architect to lead and optimise our data architecture initiatives, focusing on enhancing our industry presence within the insurance sector. This role will require you to apply your extensive experience to design, implement, and govern scalable, high-performance data solutions that meet the needs of our clients. Your expertise will foster collaboration between technical and business stakeholders, ensuring data strategies are aligned with the overall business objectives and industry best practices. You will play a crucial role in driving innovation and improving data-driven decision-making processes within the organisation and client implementations.


Key Responsibilities

  • Develop and execute comprehensive data strategies that align with business goals, focusing on the design and maintenance of scalable data architectures.
  • Implement industry-leading data governance practices to ensure data quality, integrity, and security.
  • Design solution architectures for data lakes, warehouses, and mesh frameworks, promoting seamless data access and integration.
  • Utilise cutting‑edge technologies such as Databricks, Snowflake, Data Lake, Data Warehouse and Lakehouse to build effective data infrastructure.
  • Advocate for the adoption of data mesh principles, enhancing data accessibility and flexibility across the global organisation.
  • Leverage expertise in SQL and Database Management to optimise database management and performance, supporting scalable data operations.
  • Apply your knowledge of the insurance industry to drive data initiatives that enhance operational efficiency and regulatory compliance.
  • Expert knowledge of AWS or Azure technologies.

Collaboration & Stakeholder Management

  • Work closely with clients to understand their data requirements, providing strategic insights and solutions tailored to the insurance domain.
  • Foster cross‑functional partnerships with clients, actively engaging with departments such as sales, operations, finance, underwriting, and actuarial teams.
  • Ensure alignment between technical architectures and business objectives, translating complex data concepts into actionable insights.

Continuous Improvement & Innovation

  • Proactively stay current with emerging technologies and industry trends, driving continuous improvement of the data architecture.
  • Cultivate a culture of innovation, promoting best practices and fostering a collaborative team environment.

Skills & Experience

  • Advanced experience as a Data Architect or in a similar role with specialised experience in the insurance industry.
  • Proven track record of developing and implementing successful data strategies within large, complex organisations.
  • Strong background in SQL, Data Solutions and solution architecture for data frameworks such as data lakes and warehouses.
  • Exceptional leadership, communication and interpersonal skills.
  • Strong background in Insurance domain.
  • Expertise in wealth sector and/or experience with technologies like Alaiddin, Guidewire is advantageous.
  • Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.

Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Engineering and Information Technology


Industries

Accounting


#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.

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.