Lead Enterprise Data Architect

SGN
Portsmouth
16 hours ago
Create job alert

Walton Park, London, Glasgow | Personal Contract (dependent on skills and qualifications)


Full-time | Hybrid


Competitive pension scheme – Enhanced maternity/paternity pay – Life assurance – HolidayPlus – Cycle2work Scheme & more


REQ5513


The Lead Enterprise Data Architect provides strategic leadership and technical direction for SGN’s data architecture, ensuring data is managed as a strategic asset. This role provides technical leadership across data platforms, AI and analytics, and integration capabilities, ensuring data is governed, resilient, high quality, and aligned to organisational and industry standards.


The Lead Enterprise Data Architect provides leadership and line management to the data architecture team, setting direction, priorities, developing the capability, and ensuring consistent and high-quality delivery across the data function. They will act as key advisor to business and technology leaders, shaping the future state of data architecture across SGN.


What You’ll Contribute

  • Define and maintain the enterprise data architecture vision, standards, and roadmap.
  • Develop target-state data architectures covering data models, data flows, master data, reference data, metadata, security, and data governance.
  • Ensure alignment of data strategy with enterprise architecture, digital transformation programmes, and business objectives.
  • Lead development of architectural artefacts including conceptual, logical, and physical data models.
  • Provide architectural leadership for data platforms including data warehouses, data lakes, lakehouses, and integration technologies.
  • Evaluate and recommend technologies, tools, and patterns (e.g., data mesh, event‑driven architecture, API‑first design).
  • Design scalable, secure data solutions leveraging cloud platforms (Azure, AWS, or Oracle).
  • Ensure data architecture supports advanced analytics, BI, AI/ML, and operational reporting.
  • Oversee data architecture aspects of major change initiatives and programmes.
  • Produce high-quality architecture deliverables and ensure they are integrated into delivery plans.
  • Review solution designs to ensure alignment with enterprise data standards.
  • Troubleshoot complex data issues and provide expert‑level guidance on data modelling and integration approaches.

What You Will Need

  • Extensive experience in establishing and developing an enterprise data architecture practice, ideally within complex organisations.
  • Demonstrable technical expertise spanning:
  • Cloud data platforms and ecosystems: e.g. Microsoft Azure, AWS
  • Data engineering and integration: ETL/ELT, Orchestration, Batch and streaming architectures, event driven and messaging platforms, API‑first design, Integration frameworks.
  • Database & storage technologies: SQL Server, Oracle, NoSQL, Analytical datastores
  • Data governance & compliance: data security architecture, role‑based access, encryption, regulatory frameworks (GDPR, IS27001, NIST), cloud security patterns and identity management (Azure AD)
  • Enterprise Architecture Frameworks: TOGAF, Zachman, DMBoK
  • Engineering and Scripting credibility: SQL, Python, Git & DevOps
  • Data Modelling: Conceptual, logical and physical, relational, dimensional and NoSQL, canonical and enterprise models and associated tooling.
  • To be able to build effective and collaborative relationships across a range of stakeholders, and communicate impactfully, articulating complex ideas and information clearly.
  • To be able to influence critical decisions and apply experience to interpret complex situations and offer authoritative advice.
  • Demonstrate ability to turn business problems into data designs spanning different business areas and organisational objectives, while implementing common solutions for cohesion across the estate.

Not sure you meet every requirement?

Research shows some people – particularly women and those from underrepresented backgrounds – may hesitate to apply unless they meet every criterion. At SGN, we value diverse backgrounds, experiences and perspectives.


If this role interests you but you’re not sure you tick every box, we’d still love to hear from you. You might be just who we’re looking for – now or in the future.


Why SGN?

SGN is a leader in pioneering research and development toward a net‑zero energy system. Our cutting‑edge technologies and innovative thinking are driving change in the gas industry, all while keeping people safe and warm. SGN is an award‑winning employer, including CCA Gold Awards for 'Great Places to Work' and 'Inclusivity and Accessibility'.


If you require any accommodations or support during the application process, reach out to us. We're here to help ensure an inclusive and accessible experience for everyone.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Enterprise Data Architect

Lead Enterprise Data Architect

Lead Enterprise Data Architect

Lead Enterprise Data Architect

Lead Enterprise Data Architect: Snowflake & Governance

Lead Enterprise Data Architect — Cloud, AI & Governance

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.